News featuring K S M Tozammel Hossain

Sanghani Center Student Spotlight: Sumin Kang

Graphic is the poster “Distributionally Ambiguous Multistage Stochastic Integer and Disjunctive Programs: Applications to Sequential Two-player Interdiction Games,” presented at the INFORMS Annual Meeting 2023.

Ph.D. student Sumin Kang was drawn to the Department of Industrial Systems Engineering by its prestigious faculty with particular expertise in optimization for logistics problems with uncertainty. 

“As a logistics enthusiast, I found their focus aligned very well with my own interests,” said Kang, who is advised by Manish Bansal, core faculty at the Sanghani Center.  “And the interdisciplinary environment fostering a multi-faceted approach in problem solving at the Sanghani Center added to Virginia Tech’s appeal.”

Kang’s research interests lie in optimization under uncertainty, with a focus on network optimization problems and vulnerability analysis.

“Specifically I am interested in optimization problems with incomplete information about distributions of uncertain parameters,” he said. “These optimization problems find an application in the network interdiction problem. The network interdiction problem involves a game between two players, the interdictor and the network user, where the network user aims to minimize the objective value like traveling cost and security threat level, while the interdictor aims to maximize disruption of network. 

“Solving this problem is valuable for identifying network vulnerabilities, particularly in cases of unexpected disruptions,” said Kang.  “My proposed solution approaches consider the interdictor’s varying risk appetite towards unknown distributions.”


Kang’s interest in this research began as a master’s degree student in logistics at Korea Aerospace University, when he started to struggle with logistic optimization problems, he said, because despite the prevalence of real-world uncertainty, the literature mainly focused on deterministic cases due to their high complexity. This motivated him to address the gap and contribute to the domain of optimization with uncertainty for large-scale problems.

One of Kang’s papers with his advisor, “Distributionally risk‐receptive and risk‐averse network interdiction problems with general ambiguity set,” was published in the international journal, Networks, and he presented the research at the INFORMS Annual Meeting 2022.

At the INFORMS Annual Meeting in 2023, Kang presented another of their papers, “Distributionally Ambiguous Multistage Stochastic Integer and Disjunctive Programs: Applications to Sequential Two-player Interdiction Games,” in the student poster competition and in a poster session.

Projected to graduate in 2025, he will be exploring various opportunities to continue his research. 


New spatial profiling approach maps out discoveries for future brain research

(From left) Chang Lu, the Fred W. Bull Professor of Chemical Engineering; Daphne Yao, professor of computer science; and Xiaoting Jia, associate professor in the Bradley Department of Electrical and Computer Engineering. Photo by Peter Means for Virginia Tech.

An estimated one in six people suffer from a brain disorder worldwide, according to the American Brain Foundation. Current research has provided some insight into cell-communication inside the brain, but there are still a lot of unknowns surrounding how this crucial organ functions. What if there was a comprehensive map that took into consideration not just the biology of the brain, but the specific location where the biology occurs?

Researchers in the College of Engineering have developed a powerful, cost-effective method to do just that. 

Chang Lu, the Fred W. Bull Professor of Chemical Engineering, has been leading a research project that could be groundbreaking for brain research. The newly published article in the journal Cell Reports Methods features interdisciplinary research along with faculty in two additional departments within the College of Engineering:

Their goal? Mapping and visualization of the brain biology at genome scale in the most cost-effective way possible to improve healthy functioning.

Read full story here.


Sanghani Center Student Spotlight: Medha Sawhney

Poster presentation at CV4Animals: Computer Vision for Animal Behavior Tracking and Modeling workshop during the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CPVR)

Medha Sawhney earned a bachelor’s degree from the Manipal Institute of Technology in India, where she majored in electronics and communications engineering, with a minor in data science. When considering a graduate degree in computer science, Sawhney was drawn to Virginia Tech and the Sanghani Center by a research-focused environment that offered opportunities to learn from and work with professors well known in their respective fields of research which interconnected well with her own.

“A research-focused environment makes it easy to concentrate on your work by providing interesting and challenging research projects; professors who guide you in every way; and funding opportunities via grants from organizations like the National Science Foundation,” she said. “And most professors – even if they are not your direct advisor — are extremely approachable to guide you or discuss problems.” 

Sawhney entered the university as a master’s degree student but is now pursuing a Ph.D.  She is advised by Anuj Karpatne.

Having worked in the domain of computer vision since her undergraduate years, Sawhney’s  current research is at the intersection of computer vision and mechanobiology. 

Two projects — supported by the NSF — predict the behavior and mechanics of human as well as bacteria cells. One of them involves predicting the force exerted by cells in order to be able to predict their movement using traction-force microscopy images collected in the field of mechanobiology. 

“The physics knowledge that we are integrating in our machine learning methods includes phenomenological models of cell and bacteria migration and knowledge of the mechanical forces governing interactions between cells and fiber backgrounds,” she said. 

The second project involves tracking the movement of bacteria cells to predict and also study the characteristics of their motion such as their velocity, their stickiness, and other such measures. This study is directed towards cancer research. 

“The resolution of microscopy and the dense fibrous environment the bacteria is in makes it challenging to differentiate the bacteria in an image by just looking at it since the bacteria sometimes merges with the 3D media or goes inside,” she said. “We use artificially-generated motion and temporal features of the microscopic bacteria images as input to the machine learning model to be able to identify and track them.” 

Sawhney gave a poster presentation of her work, “Detecting and Tracking Hard-to-Detect Bacteria in Dense Porous Backgrounds,” at a CV4Animals: Computer Vision for Animal Behavior Tracking and Modeling workshop during the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CPVR) last fall. 

This was preliminary work for the paper, “MEMTrack: A deep learning-based approach to microrobot tracking in dense and low-contrast environments,” which will be published in an upcoming volume of the Advanced Intelligent Systems journal.  

Sawhney will also be presenting her work on bacteria tracking at the first Workshop on Imageomics during the Association for the Advancement of Artificial Intelligence (AAAI 24) conference next week.

She is also serving on the program committee for the workshop.

Projected to graduate in 2026, her ideal job would be one that offers challenges and in which her work would have an impact on society.


Amazon Web Services, Virginia Tech Hume Center launch Emerging Technology Research Fellowship

The Cloud-based Distributed Radio Frequency Machine Learning project team members at work. Photo by Isabella Rossi for Virginia Tech.

A student-led research team is working with Amazon to advance use cases for machine learning within the cloud for wireless communication applications.

The Virginia Tech National Security Institute is collaborating with Amazon Web Services (AWS) to give 11 undergraduate students and a graduate research assistant experience deploying state-of-the-art machine learning algorithms in the cloud for distributed radio frequency spectrum sensing through the Emerging Technology Research Fellowship. The fellowship aims to improve the performance of radio frequency spectrum sensing algorithms by leveraging multiple sensors collaborating through the cloud.

The fellowship expands on the Amazon-Virginia Tech Initiative for Efficient and Robust Machine Learning that began in 2022 under the direction of the Sanghani Center for Artificial Intelligence and Data Analytics

Read full story here.


Sanghani Center Student Spotlight: Ahmed Aredah

Graphic is from the paper “Comparative analysis of alternative powertrain technologies in freight trains: A numerical examination towards sustainable rail transport”

Ahmed Aredah’s graduate school experience is not a typical one as he is simultaneously pursuing two graduate degrees in different majors. He is a master’s degree student in the Department of Computer Science advised by Hoda Eldardiry, assistant professor and core faculty at the Sanghani Center, and is also a Ph.D. student in the Bradley Department of Electrical and Computer Engineering, advised by Hesham Rakha with Eldardiry serving on his dissertation committee. 

“The multidisciplinary approach at the Sanghani Center aligns perfectly with my dual-degree aspirations, allowing me to bridge the gap between civil engineering and computer science,” Aredah said. “Advanced research facilities and extensive networking opportunities have further enriched my academic experience.”

His research area is centered on energy optimization in transportation. He is part of a team at the Virginia Tech Transportation Institute that developed NeTrainSim, a network train simulator that explores ways to make train operations more energy efficient. 

“A significant contribution from our work has been the study and proposal of different powertrain technologies to enhance train infrastructure in the United States. Thanks to the robust methodology we have employed, our findings can be expanded to other regions/countries,” Aredah said.

Their paper, “Comparative analysis of alternative powertrain technologies in freight trains: A numerical examination towards sustainable rail transport,” was recently published in the journal, Applied Energy. 

Aredah shared this research in a poster presentation at the 2023 Transportation Board Annual Meeting where he also presented the paper, “NeTrainSim: A Longitudinal Freight Train Dynamics Simulator for Electric Energy Consumption.”

His interest in energy optimization for railway systems was sparked by a combination of factors. “The real-world impact of creating more efficient, sustainable transport solutions resonated with my desire for meaningful work,” Aredah said. “And the interdisciplinary nature of the field offers a unique technical challenge that appealed to my problem-solving instincts.”

Aredah earned a bachelor’s degree and a master’s degree in civil engineering from German University in Cairo, Egypt, and nanodegrees in data science and machine learning from Udacity.

After graduating with his Ph.D. (currently projected for 2025), Aredah said that he is open to exploring any opportunity that allows him to leverage his skills.

“At my core, I am a problem solver, passionate about applying my knowledge to real-world challenges. Whether that means continuing research to push the boundaries of what’s possible or working in an industrial setting to implement practical solutions, I am eager to find a role where I can make a meaningful impact,” he said.


Three computer scientists elected fellows of the Institute of Electrical and Electronics Engineers

(From left) Chang-Tien “C.T.” Lu, Naren Ramakrishnan, and Dimitrios Nikolopoulos. Photo illustration by Peter Means for Virginia Tech.

Chang-Tien “C.T.” Lu, Dimitrios Nikolopoulos, and Naren Ramakrishnan, all faculty in the Department of Computer Science, have been elected to the 2024 class of fellows in the Institute of Electrical and Electronics Engineers (IEEE). 

To be named a fellow, IEEE members must demonstrate significant contributions to their field, show evidence of technical accomplishments and realization of significant impact to society, and a record of service to professional engineering societies, among other criteria.

Fewer than 0.1 percent of voting members in the institute are selected annually for this career milestone, according to IEEE.

Ramakrishnan is director and Lu is associate director of the Sanghani Center for Artificial Intelligence and Data Analytics.

Read full story here.


New software simulates the impact of alternative fuels for freight trains

A recent project from the Virginia Tech Transportation Institute provides new insights to the impact of freight trains using alternative fuel sources. Virginia Tech photo

Researchers at the Virginia Tech Transportation Institute recently created the nationwide multi-train simulation software, NeTrainSim, to show the impacts of a countrywide shift away from diesel.

Ahmed Aredah, a graduate student at the Sanghani Center for Artificial Intelligence and Data Analytics, is working on the project led by Hesham Rakha.


Researchers use environmental justice questions to reveal geographic biases in ChatGPT

A U.S. map shows counties where residents could (blue) or could not (pink) receive local-specific information about environmental justice issues. Photo courtesy of Junghwan Kim.

Virginia Tech researchers have discovered limitations in ChatGPT’s capacity to provide location-specific information about environmental justice issues. Their findings, published in the journal Telematics and Informatics, suggest the potential for geographic biases existing in current generative artificial intelligence (AI) models.

Ismini Lourentzou, assistant professor in the College of Engineering and core faculty at the Sanghani Center for Artificial Intelligence and Data Analytics, is a co-author on the paper. Read full story here.


Congratulations to Sanghani Center’s 2023 Summer and Fall Graduates

Virginia Tech’s 2023 Fall Commencement ceremonies take place today. The Graduate School Commencement Ceremony will be held in Cassell Coliseum at 1:30 p.m. and will be live-streamed.

“We celebrate our Summer and Fall graduates who have worked so hard to achieve their graduate degrees,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science at Virginia Tech and director of the Sanghani Center for Artificial Intelligence and Data Analytics. They deserve all the congratulations coming their way and we wish them all the best as they embark on their new journeys.”

The following Sanghani Center students are among those who are receiving degrees:

Ph.D. Graduates

Aman Ahuja, advised by Edward Fox, has earned a Ph.D. in computer science. His research focused on document understanding, search and retrieval, and question-answering to improve the accessibility of long PDF documents, such as books and dissertations. His dissertation, “Analyzing and Navigating Electronic Theses and Dissertations” was awarded the 2023 Innovative Student Thesis Award by the Networked Digital Library of Theses and Dissertations (NDLTD). Ahuja has joined DocuSign in Seattle, Washington, as an applied scientist.

Arka Daw, advised by Anuj Karpatne, has earned a Ph.D. in computer science. His research centers around the emerging field of science-guided machine learning, where machine learning models are integrated with scientific knowledge (or physics) to ensure better interpretability and generalizability while enforcing scientific consistency. The title of his dissertation is “Physics-informed Machine Learning with Uncertainty Quantification.”  Daw is joining Oak Ridge National Lab (ORNL) in Knoxville, Tennessee, as a Distinguished Staff Fellow.

Chris Grubb, advised by Leanna House, has earned a Ph.D. in statistics. His research focuses on developing a statistical learning method of population synthesis that allows for propagation of uncertainty from sample data into synthetic populations of agents. The title of his dissertation is “Inference for Populations: Uncertainty Propagation via Bayesian Population Synthesis.” Grubb has joined Virginia Tech’s Center for Biostatistics and Health Data Science in Roanoke, Virginia, as a research scientist.

Whitney Hayes, co-advised by Ashley Reichelmann and Naren Ramakrishnan, has earned a Ph.D. in sociology. Her research focus is on identity. The title of her dissertation is “Enhancing Identity Theory Measurement: A Case Study in Ways to Advance the Subfield.” Hayes also received a graduate certificate in urban computing offered through the Sanghani Center. She has joined Elevate, a climate action nonprofit based in Chicago, Illinois, and works remotely as a research analyst. 

Brian Keithadvised by Chris North, has earned a Ph.D. in computer science. His research focuses on how to represent, extract, and visualize information narratives to aid analysts in their narrative sensemaking process. The title of his dissertation is “Narrative Maps: A Computational Model to Support Analysts in Narrative Sensemaking.” Keith has joined the Catholic University of the North in Chile as an assistant professor in the Department of Computing and Systems Engineering. 

Shuo Lei, advised by Chang-Tien Lu, has earned a Ph.D. in computer science. Her research focuses on few-shot learning and domain adaptation. The title of her dissertation is “Learning with Limited Labeled Data: Techniques and Applications.” Lei has joined Sony Research in San Jose, California, as a research scientist.

Lei Zhang, advised by Chang-Tien Lu, has earned a Ph.D. in computer science. His research focuses on bi-level optimization, neural architecture search, and graph neural networks. The title of his dissertation is “Bilevel Optimization in the Deep Learning Era: Methods and Applications.”

Ming Zhu, co-advised by Daphne Yao and Ismini Lourentzou, has earned a Ph.D. in computer science. Her research focus is on Machine Learning and Natural Language Processing. The title of her dissertation is “Neural Sequence Modeling for Domain-Specific Language Processing: A Systematic Approach.” Zhu has joined ByteDance in Seattle, Washington, as a research scientist.

Master’s Degree Graduates

Nikhil Abhyankar, advised by Ruoxi Jia, has earned a master’s degree in electrical and computer engineering. His research focus is on machine learning privacy and security. The title of his master’s thesis is “Data Centric Defenses for Privacy Attacks.” Abhyankar has joined the Virginia Tech Department of Computer Science to pursue a Ph.D.

Humaid Desaiadvised by Hoda Eldardiry, has earned a master’s degree in computer science. His research focuses on enhancing the efficiency and resource utilization of Federated Learning in resource-constrained and heterogeneous environments. The title of his master’s thesis is “REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments.” Desai is joining Ellucian in Reston, Virginia, as a software engineer.

Chongyu He, advised by Edward Fox, has earned a master’s degree in computer science. His research primarily revolves around the application of advanced deep learning techniques for cell organelle segmentation in high-resolution microscopy images. The title of He’s master’s thesis is “Deep Learning Approach for Cell Nuclear Pore Detection and Quantification over High Resolution 3D Data.”

Junho Oh, advised by Lynn Abbott, has earned a master’s degree in Computer Engineering. His research focus is machine learning. The title of Oh’s master’s thesis is “Estimation of Global Illumination using Cycle-Consistent Adversarial Networks.”

Akash Sonth, advised by Abhijit Sarkar and Lynn Abbott, has earned a master’s degree in computer engineering. His research focus is on the application of machine learning in driver safety and intelligent transportation. The title of his master’s thesis is “Enhancing Road Safety through Machine Learning for Prediction of Unsafe Driving Behaviors.”  Sonth has joined the Aspen Technology office located in Bedford, Massachusetts, as a data scientist.

Surendrabikram Thapa, co-advised by Anuj Karpatne and Abhijit Sarkar, has earned a master’s degree in computer science. His research focus is multimodal learning, computer vision, and natural language processing applications. The title of his master’s thesis is “Deidentification of Face Videos in Naturalistic Driving Scenarios.” Thapa also received a graduate certificate in data analytics offered by the Sanghani Center. He has joined the Virginia Tech Transportation Institute (VTTI) as a research faculty.


‘Curious Conversations’ podcast: Ismini Lourentzou talks about AI’s potential as an assistant

“Curious Conversations” is produced by the Virginia Tech Office of Research and Innovation.

Ismini Lourentzou joined Virginia Tech’s “Curious Conversations” to chat about artificial intelligence (AI) and machine learning related to personal assistants, as well as her student team’s recent experience with the Alexa Prize TaskBot Challenge 2. 

About Lourentzou

Lourentzou is an assistant professor in the Department of Computer Science and core faculty at the  Sanghani Center for Artificial Intelligence and Data Analytics. She is also an affiliate faculty member of the National Security Institute and the Center for Advanced Innovation in Agriculture.

Read more and listen here.


Aman Ahuja garners 2023 Innovative Student Thesis Award from Networked Digital Library of Theses and Dissertations

Aman Ahuja

The Networked Digital Library of Theses and Dissertations (NDLTD) has awarded its 2023 Innovative Student Thesis Award to Aman Ahuja, who was a Ph.D. student in computer science at the Sanghani Center for Artificial Intelligence and Data Analytics.

Ahuja defended his dissertation this past summer and is currently an applied scientist at DocuSign in Seattle, Washington. His advisor was Edward Fox.

The organization’s annual award supports student efforts to transform the genre of the dissertation through the use of innovative research data management techniques and software to create multimedia Electronic Theses and Dissertations (ETDs). It includes a cash award and travel scholarship funds to attend a future ETD Symposium.  

Following is an excerpt from the email Ahuja received from the chair of the NDLTD Awards Committee notifying him of this honor:

“Your thesis, “Analyzing and Navigating Electronic Theses and Dissertations,” provides a technical framework to expand the access to the content of millions of published theses, like yours, which are constrained in their usability and usefulness by the portable document format. Current digital libraries are institutional repositories with the objective being content archiving, they often lack end-user services needed to make this valuable data useful for the scholarly community. To effectively utilize such data to address the information needs of users, digital libraries should support various end-user services such as document search and browsing, document recommendation, as well as services to make navigation of long PDF documents easier and accessible. Your research and dissertation directly addresses these concerns in creative and beneficial ways.”

Ahuja earned a bachelor’s degree in information systems from Birla Institute of Technology & Science, India, where, as part of his undergraduate studies, he was also a visiting scholar at Carnegie Mellon University in Pittsburgh, Pennsylvania. 


Teaming up to beat the heat

Assistant Professor Theo Lim of the School of Public and International Affairs presents on his research during the 2023 State of the College program. Photo by Andrew Adkins for Virginia Tech.

This summer marked the Earth’s hottest on record.

The Roanoke Valley was no exception to the heat, with news reports naming 2023 as the region’s second-hottest summer. But the rising temperatures were particularly stifling for some neighborhoods in Roanoke —  those impacted by harmful urban planning practices.

Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the Sanghani Center for Artificial Intelligence and Data Analytics, and Nathan Self, research associate at the center, are on a team of researchers led by Theodore Lim, who will use a National Science Foundation grant to work with Roanoke communities to combat the impact of rising temperatures and promote healing among those impacted by harmful urban planning practices. Read full story here.


Sanghani Center leads collaborative study to improve both discovery and traceability of illegally-sourced timber

Reference sample collections from World Forest ID

Virginia Tech has received funding from the National Science Foundation for a collaborative research project that brings machine learning and data science research to the domain of Stable Isotope Ratio Analysis (SIRA) to improve discovery and traceability of illicitly-sourced timber products. Illegal timber trade (ITT) is the most profitable natural-resource crime, valued at 50-152 billion U.S. dollars per year.

Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the Sanghani Center for Artificial Intelligence and Data Analytics, is serving as principal investigator for the project with the University of Washington, World Forest ID, and Simeone Consulting, LLC.

“To enforce timber regulations and international frameworks, there is a need for accurate, cost-effective, and high-throughput tools that can be used to identify and trace illegally sourced timber products,” Ramakrishnan said. 

The team brings together data scientists, analytical chemists, geospatial and remote sensing scientists, practitioners, international trade and supply chain specialists, and field experts who conduct reference sample expeditions to bring novel data science approaches to analyzing a range of geospatial and remotely sensed datasets.

Patrick Butler, senior research associate, and Brian Mayer, research associate at the Sanghani Center will be part of the Virginia Tech team.

Key foci of this project include machine learning methods for SIRA analytics; location determination from isotopic ratios; and active sampling strategies to close the loop. Foundational machine learning contributions in science-guided machine learning, contrastive and generative learning paradigms, and active sampling algorithms will support not only the specific domain of SIRA but other adjacent domains in environmental conservation, agricultural forecasting, and smart farm modeling. 

“For example, what we learn from our research could be directly applicable to tracing many other illicitly-sourced products and product inputs, including forest risk commodities such as cocoa, soy, and beef,” said L. Monika Moskal, professor at the University of Washington.

The study will have broad and far-reaching impacts on American security and prosperity, as well. 

“Many key U.S. adversaries rely on illegal logging to finance their activities,” said Jade Saunders, executive director at World Forest ID. “Detecting and curbing such activities will moderate sources of regional instability and threats to U.S. interests.”

The project will lead to improving geospatial prediction accuracy of product origin and will enable a cost-benefit analysis to minimize future data collection costs and optimize prediction gain. Finally, this project will also positively affect U.S. economic competitiveness by reducing competition with illicit actors and moderating risks to international trade, Ramakrishnan said.


Human-Centered Future of Work Symposium set for Nov. 3

Sue Ge, director of ICAT’s Center for Future Work Places and Practices, addresses faculty at the center’s spring networking event. Virginia Tech photo

As technology continues to revolutionize industries and alter the nature of everyday life, the future of work can seem unclear. Virginia Tech’s Institute for Creativity, Arts, and Technology (ICAT) is bringing together a wide breadth of expertise to discuss this topic during the Human-Centered Future of Work Symposium.

Sponsored by the Department of Economics and the Kohl Center, AAEC, the symposium feature a policy roundtable discussion that aims to search for the common ground on the human-centered future of work.

One of the panelists is Chris North is a professor of computer science at Virginia Tech and the associate director of the Sanghani Center for Artificial Intelligence and Data Analytics. Read full story here.


New Data and Decision Sciences Building encourages collaboration to address world’s data challenges

The Data and Decision Sciences Building. Photo by Noah Alderman for Virginia Tech.

Virginia Tech’s new Data and Decision Sciences Building has opened its doors to students, faculty, staff, and industry professionals ready to tackle some of the world’s most pressing data challenges. Completed in the summer, the 120,000-gross-square-foot facility houses multiple colleges including the Pamplin College of BusinessCollege of Engineering, and College of Science.

Several faculty from the Department of Computer Science have offices located in the building, along with labs and classrooms that allow students to experience and interact with the latest computational technologies. The new visualization lab features a high-resolution power wall with multi-touch functionality. Coupled with SAGE3 software developed by researchers in the Sanghani Center for Artificial Intelligence and Data Analytics under a $5 million dollar National Science Foundation grant, the high-resolution screen enables the display and organization of large amounts of media, data analytics, and visualizations. Read full story here.


Sanghani Center Student Spotlight: Syuan-Ying Wu

Poster for published paper “MetroScope: An Advanced System for Real-Time Detection and Analysis of Metro-Related Threats and Events via Twitter”

Metro systems are vital to many people’s daily lives, but they face safety or reliability challenges, such as criminal activities or infrastructure disruptions. Real-time threat detection and analysis are crucial to ensure their safety and reliability. 

Syuan-Ying (Justin) Wu, a master’s degree student in computer science whose research focuses on social media analytics and software development, is currently part of a research team that is working with the Washington Metropolitan Area Transit Authority (WMATA) to address these issues.  

With fellow students at the Sanghani Center and his advisor, Chang-Tien Lu, Wu has been instrumental in developing the MetroScope real-time threat/event detection system that can automatically analyze event development; prioritize events based on urgency; send emergency notifications via emails; provide efficient content retrieval; and self-maintain the system.

“This is a great improvement over many existing systems that can detect the event but cannot analyze it or prioritize it,” Wu said. “And our system offers other advantages like not having to continuously monitor system notifications.”

Their collaborative paper, “MetroScope: An Advanced System for Real-Time Detection and Analysis of Metro-Related Threats and Events via Twitter,” was published in the proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval held in Taipei, Taiwan, this past summer.

Wu, who earned a bachelor’s degree in applied mathematics at Fu Jen Catholic University in Taiwan, said this research collaboration with a metropolitan metro system is a good example of what led him to pursue his master’s degree at Virginia Tech and the Sanghani Center. “The exceptional computer science program and distinguished professors have offered me the opportunity to find ways of applying cutting-edge technology to tackle a real-world problem,” he said. “It has been the perfect environment to achieve my goals.”

Projected to graduate this fall, Wu hopes to secure a position as a software engineer. 


Ming Jin receives NSF grant to introduce antifragility into power systems

Ming Jin

Ming Jin, an assistant professor in electrical and computer engineering and core faculty at the Sanghani Center has received a National Science Foundation grant to revolutionize the design of learning-enabled, safety-critical systems, with a special focus on power systems.

The grant was awarded under the Safe Learning-Enabled Systems (SLES), a partnership between the NSF, Open Philanthropy, and Good Ventures.

Jin will collaborate with Javad Lavaei, professor in Industrial Engineering and Operations Research at the University of California Berkeley.

The project introduces antifragility, a concept that goes beyond robustness which can be compared to a sturdy structure that remains unyielding in a storm but does not grow or adapt from the experience; or resilience which is like a rubber band: when stretched, it can recover by going back into its original shape. 

“We are not merely designing systems to withstand challenges of rare and unpredictable events, but to flourish because of them,” Jin said. 

The task of preserving end-to-end safety of the power system will be crucial, Jin said, though it is complex amidst distributional shifts, driven by the growing complexity and unpredictability of the environment. 

The project will addresses safety challenges through three interconnected research thrusts. The first thrust targets the creation of proactive, antifragile systems that anticipate and adapt to changes, using advanced techniques such as meta-safe learning and offline reinforcement learning. The second thrust bolsters system antifragility through multi-agent systems, encouraging exploration, cooperation, and distributed control to ensure resilience and safety, even under significant disturbances. The third thrust is devoted to validation and stress testing, employing multi-objective adversarial learning and real-world case studies to better handle rare or unexpected events.

“Our algorithms are more than just learners; they’re evolvers. By turning continual threats into avenues for enhancement, we are redefining what safety in power systems looks like,” he said.

Four students advised by Jin will work with him on the project: Vanshaj KhattarAhmad Al-TawahaZain ul Abdeen, andBilgehan Sel.


Amazon-Virginia Tech Initiative announces support for two Amazon Fellows and five faculty-led projects for 2023-24 academic year

The Amazon Fellows are (from left) Minsu Kim and Ying Shen. Photos courtesy of the subjects.

The Amazon–Virginia Tech Initiative for Efficient and Robust Machine Learning will support two Amazon Fellows and five innovative research projects led by Virginia Tech faculty in the 2023-24 academic year that further the initiative’s mission of advancing innovation in machine learning. 

The initiative, launched in 2022, is funded by Amazon, housed in the College of Engineering, and directed by researchers at the Sanghani Center for Artificial Intelligence and Data Analytics on Virginia Tech’s Blacksburg campus and at the Virginia Tech Innovation Campus in Alexandria. 

An open call for fellowship nominations and faculty projects went out across the Virginia Tech campuses. An advisory committee of Virginia Tech faculty and Amazon researchers selected two Amazon Fellows from 27 nominations — more than double what was received last year — and five faculty projects from 17 submitted proposals. Read full story here.


Faculty ‘cautiously optimistic’ about the potential of generative AI

Faculty members are learning that generative AI tools are capable of many things: writing essays and emails, customizing lessons and learning, even creating seemingly original art, like this impressionistic painting of a laptop. Illustration created by Melody Warnick using AI.

Faculty are considering how AI models such as ChatGPT can customize learning by producing dynamic case studies or offering instant feedback or follow-up questions. Many are making AI the subject of assignments. They’re asking students to analyze and identify weaknesses in arguments produced by ChatGPT, for instance, or to edit an AI-produced essay with “track changes” on.

That kind of critical thinking about generative AI is vital, said Ismini Lourentzou, assistant professor of computer science in the College of Engineering and core faculty at the Sanghani Center for Artificial Intelligence and Data Analytics. “It’s our responsibility as educators to teach students how to use these tools responsibly, and then understand the limitations of these tools.” Read the full story here.


Sanghani Center Student Spotlight: Vanshaj Khattar

Graphic is from the paper ” Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance”

Vanshaj Khattar, a Ph.D. student in electrical engineering, is passionate about use-inspired research and solving real-world problems. 

“More specifically, I am interested in how we can design trustworthy reinforcement learning algorithms that are safe, robust, explainable, and can continually adapt to non-stationarity in the real world,” said Khattar, who is advised by Ming Jin.

Currently, he is working on an offline reinforcement learning (RL) problem for building energy management, where the learning agent has to learn optimal actions from a dataset without access to the environment. 

“Offline RL is hard because not all possible cases are covered inside the dataset,” Khattar said. “I am addressing this partial coverage by proposing an implicit actor-critic method for offline RL using optimization-based policies with a special robustness property to learning errors in offline RL which I am able to exploit to achieve a good performance on a multiple-building energy management problem. At the same time, I am maintaining some key aspects of interpretability which are lacking in current approaches.” 

Khattar earned a bachelor’s of technology degree in electrical and electronics engineering from Delhi Technological University, India, and earned a master’s degree in electrical engineering from Virginia Tech.

While in the master’s program, he worked on motion prediction/planning for autonomous vehicles and came across reinforcement learning methods and their huge successes in many domains such as AlphaGo. 

“However, I realized that the potential of RL methods was mostly being utilized in simulated domains, and real-world applications were still limited,” Khattar said. “This inspired me to pursue my research in building trustworthy RL algorithms that can be applied to real-world applications with safety guarantees.”

He said the opportunity to collaborate was a major factor in attracting him to Virginia Tech and the Sanghani Center.

In 2023, he has presented three papers: “Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance” and “On Solution Functions of Optimization: Universal Approximation and Covering Number Bounds,” both at the 37th AAAI Conference on Artificial Intelligence; and “A CMDP-within-online framework for Meta-Safe Reinforcement Learning,” at the International Conference on Learning Representations(ICLR).

Khattar is projected to graduate in 2026 and hopes to continue his research as an industry professional. 


Sanghani Center Student Spotlight: Wenjia Song

Graphic is from the paper “Subpopulation-specific Machine Learning Prognosis for Underrepresented Patients with Double Prioritized Bias Correction”

Cyberattacks have led to substantial losses for both businesses and individual users in recent years raising an urgent need to strengthen protection against such threats,” said Wenjia Song, a Ph.D. student in computer science who is working to address the problem.

“My research focuses on machine learning application and methodology development for improving accuracy on crucial detection problems, including medical predictions and threat detection in cybersecurity, through quantitative experiments,” she said.

Song’s current project is aimed at cyber threat detection. 

“Real-world examples of such attacks include the Colonial Pipeline ransomware attack and the SolarWinds hack,” she said. “We try to detect malicious attack behaviors at an early stage in order to minimize the damage they may cause.”

Prior to entering the doctoral program, Song earned two bachelor of science degrees – in computer science and in mathematics — from Virginia Tech. 

“As a Ph.D. student, I was attracted to the Sanghani Center because of its reputation for diverse and impactful research,” she said. “I really like being part of a thriving academic community where I receive significant encouragement and support from both professors and my peers.”

Song is advised by Danfeng (Daphne) Yao.

Among her published papers are: “Subpopulation-specific Machine Learning Prognosis for Underrepresented Patients with Double Prioritized Bias Correction,” in Communications Medicine in 2022; and “Specializing Neural Networks for Cryptographic Code Completion Applications,” in IEEE Transactions on Software Engineering in 2023.

Song also presented her work on measurement of ransomware behaviors and evaluation of defenses at both the Commonwealth Cyber Initiative (CCI)/Virginia Tech Transportation Institute (VTTI) Tech Showcase in 2022 and at the CCI Symposium in 2023.

In 2022, she presented the poster “APT Detection through Sensitive File Access Monitoring” at the Network and Distributed System Security (NDSS) Symposium and the poster “Behavioral Characterization of Crypto-Ransomware and Evaluation of Defenses” at the IEEE Secure Development Conference. 

She also gave a lightning talk, “Crypto-ransomware Detection through Quantitative API-based Behavioral Profiling,” at USENIX Security 2023. 

Projected to graduate in May 2024, Song said she would like to continue her research in an industry position.


Making a CAREER on bridging scientific knowledge and AI

Anuj Karpatne. Photo by Peter Means for Virginia Tech.


Anuj Karpatne,
associate professor in the Department of Computer Science in the College of Engineering has won a five-year, $595,738 National Science Foundation Faculty Early Career Development Program CAREER award to explore a unified approach for accelerating scientific discovery using scientific knowledge and data. Karpatne is also a core faculty member at the Sanghani Center for AI and Data Analytics. Read the full story here.


Discussions on higher education issues, universitywide priorities frame quarterly board meeting; final design for Mitchell Hall approved

Lee Learman, dean of the Virginia Tech Carilion School of Medicine, leads members of the Virginia Tech Board of Visitors on a tour of the facility in Roanoke during the board’s August meeting. Photo by Ryan Anderson for Virginia Tech.

The Virginia Tech Board of Visitors held its latest quarterly full-board meeting Sunday through Tuesday at the W.E. Skelton 4-H Educational Conference Center in Wirtz and at the Fralin Biomedical Research Institute at VTC in Roanoke.

Following an orientation session Sunday morning, board members engaged in a retreat to discuss issues facing Virginia Tech and higher education in general. To begin the retreat, three experts led a conversation on generative artificial intelligence (AI) and its impact on higher education and society more broadly: Naren Ramakrishnan, Virginia Tech’s Thomas L. Phillips Professor of Engineering and director of the Sanghani Center for AI and Data Analytics; Scott Hartley, co-founder and managing partner of Everywhere Ventures, a pre-seed venture capital firm, and best-selling author of “The Fuzzy and the Techie: Why the Liberal Arts Will Rule the Digital World”; and Rishi Jaitly, professor of practice and Distinguished Humanities Fellow at Virginia Tech, where he leads the Institute for Leadership in Technology. Read full story here.


Innovation Campus solidifies plans for faculty recruitment, research areas of focus, and curriculum

Supported through a three-year seed grant from Fralin Life Sciences Institute, a group of 14 interdisciplinary researchers led by Peter Vikesland will develop wireless sensor networks to survey microbial threats to water quality. Photo by Ryan Young for Virginia Tech.

Atop a new wave of support from the Fralin Life Sciences Institute, Peter Vikesland, the Nick Prillaman Professor of Civil and Environmental Engineering, is leading a research team in creating wireless sensor networks to survey microbial threats to water quality and to enable operational control and provide real-world feedback for public transparency. The project, Technology-enabled Water Surveillance and Control, reflects the “one water” concept that views water quality as important to our society, economy, and environment and requires an integrated approach to policy planning and implementation.

Lenwood Heath, professor of computer science and core faculty at the Sanghani Center for Artificial Intelligence and Data Analytics, will develop algorithms for locating sensors and designing networks for optimal benefit. Read full story here.


¿Qué piensas? Graduate School program provides platform for idea-sharing with Latin American visitors

A group from the University of San Francisco d’Quito in Ecuador explored the research taking place at the Fralin Biomedical Research Institute at VTC in Roanoke. The visit was part of an educational exchange organized by Virginia Tech’s Graduate School. Photo by Leigh Anne Kelley for Virginia Tech.

Academics from the University of San Francisco d’Quito in Ecuador were hosted by Aimée Surprenant, dean of the Graduate School, as part of the Future Professoriate Group from Latin America.

Among those they heard from on campus was Naren Ramakrishnan, the Thomas L. Phillips Professor of engineering at Virginia Tech, founder and director of the Sanghani Center for Artificial Intelligence and Data Analytics and director of the Amazon-Virginia Tech Initiative in Efficient and Robust Machine Learning. Read full story here.


Children’s National Hospital, Virginia Tech unite to advance AI for pediatric health

Subha Madhavan, vice president and head of clinical artificial intelligence/machine learning with biopharmaceutical company Pfizer, stressed the need to use artificial intelligence methods to understand children’s health at a meeting of scientists and innovators led by Children’s National Hospital and the Virginia Tech Sanghani Center for Artificial Intelligence and Data Analytics. The brainstorming took place on the Children’s National Research & Innovation Campus in Washington, D.C.

“Start by determining the problem you desire to solve, then decide on the technology to solve it,” said Subha Madhavan, vice president and head of clinical artificial intelligence/machine learning with global biopharmaceutical company Pfizer. 

Madhavan was the keynote speaker at AI for Pediatric Health and Rare Diseases, an inter-institutional meeting of scientists and innovators co-led by Children’s National Hospital and the Virginia Tech Sanghani Center for Artificial Intelligence and Data Analytics to discuss the potential of artificial intelligence (AI) to understand pediatric health.

The pressing issue at the gathering at the Children’s National Research & Innovation Campus in Washington, D.C., involved tackling diseases, particularly cancer, in children, an area that suffers from limited treatment options and inadequate research compared with diseases affecting adults.  Read full story here.


Sanghani Center graduate students gain real-world experience while working at companies and labs from coast to coast

Ph.D. student Jianfeng He is an applied scientist intern at Amazon AWS in Seattle, Washington

Summer offers an opportunity for graduate students at the Sanghani Center to gain real-world experience in their research focus areas by working at major companies and labs across the country. This year these include places like Amazon AWS in Seattle, Washington; JPMorgan Chase & Co in New York City;  the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) in Cambridge, Massachusetts; Bosch in Pittsburgh, Pennsylvania; and the Intel Lab in Santa Clara, California.  

Following is a list of Sanghani Center students – where they are and what they are doing:

Satvik Chekuri, a Ph.D. student in computer science, is a natural language processing research intern working remotely with a Deloitte Audit and Assurance Data Science team in New York City. The team’s research focuses on the intersection of knowledge graphs and Large Language Models (LLMs) in the financial domain. His advisor is Edward A. Fox.

Hongjie Chen, a Ph.D. student in computer science, is a research scientist intern at Yahoo Research in Sunnyvale, California, working remotely with the advertising team. His advisor is Hoda Eldardiry.

Humaid Desaia master’s degree student in computer science, is a software engineer intern at Ellucian in Reston, Virginia, working remotely. He is contributing to Ellucian’s SaaS-based solutions using React.js, Node.js, and AWS cloud technologies. His advisor is Hoda Eldardiry.

Jianfeng He, a Ph.D. student in computer science, is an applied scientist intern working onsite at Amazon AWS in Seattle, Washington, where he is researching text summarization. His advisor is Chang-Tien Lu.

Adheesh Juvekar, a Ph.D. student in computer science, is an applied scientist intern working on generative artificial intelligence onsite at Amazon in Boston, Massachusetts. His advisor is Ismini Lourentzou.

Myeongseob Ko, a Ph.D. student in electrical and computer engineering, is a machine learning research intern onsite at Bosch in Pittsburgh, Pennsylvania, where he is working on a diffusion model. His advisor is Ruoxi Jia.

Shuo Lei, a Ph.D. student in computer science, is a graduate research intern onsite at Intel Labs in Santa Clara, California. She is working on developing a new few-shot learning method for multi-modal object detection to lower the effort of human annotation, training effort, and domain adaptation while meeting accuracy requirements for industrial usage. Her advisor is Chang-Tien Lu.

Wei Liu, a Ph.D. student in computer science, is a business intelligence intern at Elevance Health in Indianapolis, Indiana, working remotely with the data analysis team. Her advisor is Chris North.

Amarachi Blessing Mbakwe, a Ph.D. student in computer science, is an artificial intelligence research associate intern at JPMorgan Chase & Co in New York City, working onsite. She is conducting research on natural language processing-related problems that involve applying Large Language Models (LLMs) in finance. Her advisor is Ismini Lourentzou.

Makanjuola Ogunleye, a Ph.D student in computer science is a data scientist intern at Intuit, working onsite with the company’s AI Capital team in Mountain View, California. He is contributing to key machine learning products. His advisor is Ismini Lourentzou.

Mandar Sharma, a Ph.D. student in computer science, is a Ph.D. software engineering intern at Google AI in Kirkland, Washington, where he is working onsite on integrating state-of-the-art in natural language processing to the services provided by Google’s Cloud AI platforms. His advisor is Naren Ramakrishnan.

Ying Shen, a Ph.D. student in computer science, is a research intern onsite at Apple in New York City, where she is working on diffusion models. Her co-advisors are Lifu Huang and Ismini Lourentzou.

Afrina Tabassum, a Ph.D. student in computer science, is a research intern at Microsoft in Redmond, Washington, working onsite. She is co-advised by Hoda Eldardiry and Ismini Lourentzou.

Chiawei Tanga master’s degree student in computer science, is a software engineer intern onsite at Juniper Network in Sunnyvale, California. His work involves creating a simulator designed to emulate the data output from wired network devices such as routers and switches. This strategic initiative facilitates system scalability testing for developers and significantly mitigates the financial impact associated with the procurement of physical hardware. His advisor is Chris Thomas.

Muntasir Wahed, a Ph.D. student in computer science, is a research intern onsite at IBM Research Almaden Lab in San Jose, California, working on the development and application of foundation models. His advisor is Ismini Lourentzou.

Zhiyang Xu, a Ph.D. student in computer science, is an applied scientist intern onsite at Amazon Alexa in Sunnyvale, California, where he is working on improving dialogue systems. His advisor is Lifu Huang.  

Raquib Bin Yousuf, a Ph.D. student in computer science, is among 25 students from 19 colleges chosen to attend the Washington Post Engineering class in Washington, D.C., this summer. He is working with state of the art artificial intelligence systems to develop new technology for the Washington Post. His advisor is Naren Ramakrishnan.

Yi Zenga Ph.D. student in computer engineering, is a research scientist intern onsite at Meta in Menlo Park, California, working on artificial intelligence fairness, finding ways to make state of the art AI systems more robust and responsible. His advisor is Ruoxi Jia.

Jingyi Zhang, a Ph.D. student in computer science, is a graduate intern working remotely with Amgen’s Computational Biology Group within Clinical Biomarkers & Diagnostics in Thousand Oaks, California. She is taking an active role in developing a data and analytics platform as well as participating in prostate therapeutic area translational computational biology. Her advisor is Lenwood Heath.

Shuaicheng Zhang, a Ph.D. student in computer science, is a summer intern onsite at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) in Cambridge, Massachusetts, where he is conducting research on the generative graph foundation model. His advisor is Dawei Zhou.

Xiaona Zhou, a Ph.D. student in computer science, is a University Research Association Sandia Graduate Summer Fellow at Sandia National Labs in Livermore, California. She is onsite working on anomaly detection in time series data. Her advisor is Ismini Lourentzou.


Innovation Campus solidifies plans for faculty recruitment, research areas of focus, and curriculum

A rendering of the Innovation Campus Academic Building One, opening in fall 2024.

In his biannual presentation to the Virginia Tech Board of Visitors, Lance Collins, vice president and executive director of the Virginia Tech Innovation Campus, updated the board this month on progress with the Virginia Tech Innovation Campus faculty recruitment, research areas of focus, and curriculum development.

Collins said the Innovation Campus faculty are strong collaborators, bringing with them established relationships with business, the tech industry, and government. He highlighted faculty-led centers and initiatives, such as the Sanghani Center for Artificial Intelligence and Data Analytics directed by Naren Ramakrishnan and an up-and-coming entrepreneurship track led by Angelos Stavrou with support from local investors, as strengths of the Innovation Campus community. Read the full story here.


Congratulations to Sanghani Center’s 2023 Spring Graduates

Virginia Tech’s 2023 Commencement ceremonies are underway culminating with the university commencement in Blacksburg on Friday, May 12, and commencement in the Washington D.C. area on Sunday, May 14.

“Once again we have come to that bittersweet time when we say farewell to our graduating students at the Sanghani Center and wish them continued success as they take the next step in meeting their long-term goals,”  said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science at Virginia Tech and director of the Sanghani Center for Artificial Intelligence and Data Analytics. “We take pride in their hard work and accomplishments and in knowing that they are well prepared to meet real-world challenges.”

The following Sanghani Center students are among the 284 Ph.D. and 1,205 master’s students receiving degrees this Spring.

Ph.D. Graduates

Badour AlBahar, co-advised by Jia-Bin Huang and Lynn Abbott, has earned Ph.D. in electrical and computer engineering. Her research interests lie in computer vision and computer graphics and more specifically, image synthesis. The title of her dissertation is “Controllable Visual Synthesis.” AlBahar is joining Kuwait University in Kuwait City, Kuwait, as an assistant professor.


Jonathan Baker
advised by Mark Embree, has earned a Ph.D. in math. His research interests lie in spectral theory in linear dynamics and control, passive source localization, and machine learning. The title of his dissertation is “Vibrations of mechanical structures: source localization and nonlinear eigenvalue problems for mode calculation.” Baker also received the graduate certificate in Urban Computing.


Jie Bu
, advised by Anuj Karpatne, has earned a Ph.D. in computer science. His research interest lies in machine learning, particularly in science-guided machine learning, representation learning, and network pruning. The title of his dissertation is “Achieving More with Less: Learning Generalizable Neural Networks With Less Labeled Data and Computational Overheads.” Bu is joining Apple in Cupertino, California, as a machine learning engineer. 

Nurendra Choudhary, advised by Chandan Reddy, has earned a Ph.D. in computer science. His research focus is learning representations for knowledge graphs and natural language by utilizing auxiliary information such as relational structures. The title of his dissertation is “Multimodal Representation Learning for Textual Reasoning over Knowledge Graphs”. Choudhary is joining Amazon in Palo Alto, California, as an applied scientist II.

Mohannad Elhamod, advised by Anuj Karpatne, has earned a Ph.D. in computer science. His research interest is in machine learning in general and, more specifically, in knowledge-guided machine learning. The title of his dissertation is “Understanding The Effects of Incorporating Scientific Knowledge on Neural Network Outputs and Loss Landscapes.” He also received a Graduate Student of the Year Award from the Virginia Tech Graduate School and was one of three speakers at the Graduate School commencement ceremony. Elhamod is joining Questrom School of Business at Boston University in Boston, Massachusetts, as a clinical assistant professor.

Melissa Tilashalski, co-advised by Leanna House and Kimberly Ellis, has earned a Ph.D. in industrial systems engineering. Her research focus is urban computing. The title of her dissertation is “Influence of Customer Locations on Heuristics and Solutions for the Vehicle Routing Problem.” Tilashalski is joining Johns Hopkins University in Baltimore, Maryland, as a lecturer.

Master’s Degree Graduates

Hirva Bhagat, co-advised by Lynn Abbott and Anuj Karpatne, has earned a master’s degree in computer science. Her research focus is on improving driver gaze estimation for driver safety applications. The title of her thesis is “Harnessing the Power of Self-Training for Gaze Point Estimation in Dual Camera Transportation Datasets.” Bhagat will be joining Goldman Sachs in Dallas, Texas, as an analyst in the company’s Risk Engineering Division. 


Elizabeth Christman
, advised by Chris North, has earned a master’s degree in computer science. Her research interests lie in data analytics and finding ways to visualize and explore big data. The title of her master’s thesis is “2D Jupyter: Design and Evaluation of 2D Computational Notebooks.” Christman is joining Leidos in Bethesda, Maryland, as a software engineer.

Rebecca DeSipio, advised by Lynn Abbott, has earned a master’s degree in electrical and computer engineering. Her research focuses on machine learning and deep learning methods for image classification. The title of her thesis is “Parkinson’s Disease Automated Hand Tremor Analysis from Spiral Images.” She will be joining GA-CCRi in Charlottesville, Virginia, as a data scientist. 

Yogesh Deshpande advised by Lynn Abbott, has earned a master’s degree in electrical and computer engineering. His research is focused on exploring and implementing non-invasive techniques to retrieve human body parameters specifically on the usage of computer vision and deep learning methods to address the scope of human authentication based on iPPG signals. The title of his master’s thesis is “Camera-based Recovery of Cardiovascular Signals from Unconstrained Face Videos Using an Attention Network.”

Dhanush Dinesh, advised by Edward Fox, has earned a master’s of engineering degree in computer engineering. His research focus is on developing infrastructure on the cloud to support the processing of large datasets. The title of his thesis  is “Utilizing Docker and Kafka for Highly Scalable Bulk Processing of Electronic Thesis and Dissertation (ETDs).” Dhanush has joined Citibank in Irving, Texas, as a senior DevOps engineer.

Hulya Dogan, advised by Ismini Lourentzou, has earned a master’s degree in computer science. Her research interests are social media analysis, machine learning, and natural language processing. The title of her thesis is “Narrative Characteristics in Refugee Discourse: An Analysis of American Public Opinion on Afghan Refugee Crisis After the Taliban Takeover.”  Dogan is joining Moog Inc. in Blacksburg, Virginia, as a data analyst and will continue her Fellowship with the CDC in Atlanta in the division of Health Informatics. 

Naveen Gupta, advised by Anuj Karpatne, has earned a master’s degree in computer science. His research interest lies in the physics guided machine learning field. The title of his thesis is “Solving Forward and Inverse Problems for Seismic Imaging using Invertible Neural Networks.”  Gupta is joining Hughes Communication in Germantown, Maryland, as an MTS 3 – software engineer.


Sahil Hamal is advised by Chris North, has earned a master’s degree in computer science. His research focus is visual analytics and explainable artificial intelligence. The title of his master’s thesis is “Interpreting Dimensions Reductions through Gradient Visualization.” Hamal also received the Paul E. Torgersen Research Excellence Award.

Meghana Holla, advised by Ismini Lourentzou, has earned a master’s degree in computer science. Her research focuses on machine learning and deep learning applied to natural language processing and multimodal problems at the intersection of language and vision. The title of her thesis is “Commonsense for Zero-Shot Natural Language Video Localization.” Holla also received the Paul E. Torgersen Research Excellence Award. She is joining Bloomberg LP in New York City as a machine learning engineer.


Sanjula Karanam
, advised by Danfeng (Daphne) Yao, has earned a master’s degree in computer engineering. Her research focuses on detecting ransomware and benign files on a Windows machine using their behavioral aspects, more specifically dynamic function calls made by a file during execution. The title of her thesis is “Ransomware Detection Using Windows API Calls and Machine Learning.”

Gaurang Karwandeadvised by Ismini Lourentzou, has earned a master’s degree in electrical and computer engineering. His research focus is in the field of artificial intelligence and its applications in healthcare, more specifically medical imaging and precision medicine. The title of his master’s thesis is “Geometric Deep Learning for Healthcare Applications.” Karwande is joining VideaHealth, Inc. in Boston, Massachusetts, as a machine learning engineer.

Fulan Li, advised by Lynn Abbott, has earned a master’s degree in electrical and computer engineering. His research focuses on extracting PPG signals from human face video using machine learning models. The title of his master’s thesis is “A Temporal Encoder-Decoder Approach to Extracting Blood Volume Pulse Signal Morphology from Face Videos.”


Xiaochu Liadvised by Lifu Huang, has earned a master’s degree in computer science. His research focus is deep learning-based natural language processing and information extraction, especially in entity linking and event extraction in the biomedical domain. The title of his thesis is “Joint Biomedical Event Extraction and Entity Linking via Collaborative Training.”

Javaid Akbar Manzoor, advised by Edward Fox, has earned a master’s degree in computer science. His research focus is on exploring how to use deep learning to segment long scientific documents into chapters. The title of his thesis is “Segmenting Electronic Theses and Dissertations By Chapters.”  Manzoor has joined Lightcast in Boston, Massachusetts, as a data scientist. 

Avi Seth, advised by Ismini Lourentzou, has earned a master’s degree in computer science. His research is focused on active learning and generative models. The title of his thesis is “Data Sharing and Retrieval of similar Manufacturing Processes.”

Aditya Shah, advised by Edward Fox, has earned a master’s degree in computer science. His research focus is on using Large Language Models (LLMs) for different downstream applications. The title of his master’s thesis is “Leveraging Transformer Models and Elasticsearch to Help Prevent and Manage Diabetes through EFT Cues.” Shah is joining Capital One Headquarters in McLean, Virginia, as a senior data scientist.

Rutuja Tawareadvised by Naren Ramakrishnan, has earned a master’s degree in computer science. Her research is focused on analyzing the behavior of transformers when they deal with math problems, specifically in a few-shot setting. The title of her thesis is “A Study of Pretraining Bias and Frequencies in Language Models.”  


Class of 2023: Mohannad Elhamod and Alli Rossi-Alvarez named Graduate Students of the Year

Alli Rossi-Alvarez (at left) and Mohannad Elhamod, both in the College of Engineering, were named the 2023 Graduate Students of the Year by the Virginia Tech Graduate School. Photo by Peter Means for Virginia Tech.

Two graduate students in the College of Engineering have been recognized by the Virginia Tech Graduate School for their exemplary work inside and outside the classroom.

Mohannad Elhamod in computer science and Alli Rossi-Alvarez in industrial and systems engineering both received the 2023 Graduate Student of the Year award. This award recognizes students for their character, service, outstanding contributions, and academic achievements. 

Elhamod is also a student at the Sanghani Center for Artificial Intelligence and Data Analytics, advised by Anuj Karpatne, core faculty at the center. Read full story here.


Sanghani Center Student Spotlight: Longfeng Wu

Graphic is from the paper “Towards High-Order Complementary Recommendation via Logical Reasoning Network”

An interest in finding some unknown patterns from existing data influenced Longfeng Wu’s research focus. Wu, who is advised by Dawei Zou, is pursuing her doctoral degree in computer science working on symbolic reasoning and trustworthy graph learning. 

“I am focused on exploring the reasoning process and developing more reliable and trustworthy models in the real world,” Wu said. “Considering that current knowledge graphs are massive and incomplete, symbolic reasoning over graphs could deduct new facts from existing data through representation learning. For example, in recommendation systems, the representation of products could reflect the relationships between them.”

She presented “Towards High-Order Complementary Recommendation via Logical Reasoning Network” at the IEEE International Conference on Data Mining (ICDM-2022) this past November. 

Wu received a bachelor’s degree in information and computing science and a master’s degree in information science, bothfrom Nanjing Agricultural University, China. In choosing a university for her Ph.D. she was attracted to Virginia Tech for its outstanding computer science program, distinguished professors, and collaborative atmosphere.

“I am honored to be part of the Sanghani Center community where the guidance and support of professors allow and encourage me to do the work that I find interesting and meaningful,” Wu said. 

Projected to graduate in Spring 2026, Wu said her long-term goal is to continue her current research in some capacity. “Artificial Intelligence will be widely adopted in the future and can extensively promote social development and enhance social welfare. I would like make a contribution to this process.”


Lifu Huang receives NSF CAREER award to lay new ground for information extraction without relying on humans

Lifu Huang. Photo by Peter Means for Virginia Tech.

Considering the millions of research papers and reports from open domains such as biomedicine, agriculture, and manufacturing, it is humanly impossible to keep up with all the findings.

Constantly emerging world events present a similar challenge because they are difficult to track and even harder to analyze without looking into thousands of articles. 

To address the problem of relying on human effort in situations such as these, Lifu Huang, an assistant professor in the Department of Computer Science and core faculty at the Sanghani Center for Artificial Intelligence and Data Analytics, is researching how machine learning can extract information without relying on humans.  Read the full story here.


Researchers study the crowdsourced investigation of Jan. 6, 2021

Kurt Luther is an associate professor of computer science and history. Photo by Olivia Coleman for Virginia Tech.

How has online sleuthing successfully replaced wanted posters?

Researchers within the Virginia Tech Department of Computer Science answered this question by studying the crowdsourced online investigation that followed the Jan. 6, 2021, insurrection at the U.S. Capitol.

Tianjiao “Joey” Yu and Kurt Luther collaborated on the project with Ismini Lourentzou, assistant professor of computer science and a core faculty at the Sanghani Center for Artificial Intelligence and Data Analytics, and Sukrit Venkatagiri, a postdoctoral researcher at the University of Washington. Read the full story here.


Amazon-Virginia Tech Initiative showcases innovative approaches to robust and efficient machine learning

(From left) Reza Ghanadan, senior principal scientist, Amazon Alexa and the new Amazon center liaison for the Amazon-Virginia Tech initiative; Shehzad Mevawalla, vice president of Alexa Speech Recognition, Amazon Alexa; Virginia Tech President Tim Sands; Lance Collins, vice president and executive director, Innovation Campus; Julie Ross, the Paul and Dorothea Torgerson Dean of Engineering; Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the Amazon-Virginia Tech initiative; and Wanawsha Shalaby, program manager for the Amazon-Virginia Tech initiative. Photo by Lee Friesland for Virginia Tech.

Virginia Tech and Amazon gathered for a Machine Learning Day held at the Virginia Tech Research Center — Arlington on April 25 to celebrate and further solidify their collaborative Amazon–Virginia Tech Initiative for Efficient and Robust Machine Learning.  

Announced last year, the initiative — funded by Amazon, housed in the College of Engineering, and directed by researchers at the Sanghani Center for Artificial Intelligence and Data Analytics on Virginia Tech’s campus in Blacksburg and at the Innovation Campus in Alexandria — supports student- and faculty-led development and implementation of innovative approaches to robust machine learning, such as ensuring that algorithms and models are resistant to errors and adversaries, that could address worldwide industry-focused problems. Read full story here.


Sanghani Center Student Spotlight: Amarachi Blessing Mbakwe

Graphic is from the paper “CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays”

In her research at the Sanghani Center, Ph.D. student Amarachi Blessing Mbakwe is trying to develop advanced artificial intelligence methodologies for better medical imaging and clinical decision-making.

Her passionate drive to improve healthcare systems that could save millions of lives worldwide stems from personal experience. With the deaths of two close family members in her home region in Nigeria, Mbakwe witnessed firsthand the devastating consequences of delayed disease detection, poor treatment management, and a shortage of healthcare professionals. 

Targeted intervention can improve healthcare access for everyone and mitigate the disparities in clinical care often faced by underrepresented populations and minorities, said Mbakwe, who is advised by Ismini Lourentzou.

“By developing an AI algorithm that can accurately and quickly analyze chest x-rays, my research can help reduce the time and effort required for radiologists to interpret medical imaging tests which, in turn, can help ensure timely patient treatment or adjustment of treatments, especially in regions with a shortage of radiologists,” she said.

Mbakwe has published papers and articles in various journals and conferences. She presented a collaborative paper, “CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays,” at the 2022 Medical Image Computing and Computer Assisted Intervention Society conference in Singapore and, this spring, at the Computing Research Association  2023 CRA-WP Grad Cohort Workshop for IDEALS in Hawaii and the 2023 Grad Cohort Workshop for Women.

CheXRelNet incorporates local and global visual features, utilizes inter-image and intra-image anatomical information, and learns dependencies between anatomical region attributes via graph attention to accurately predict disease progression for a pair of chest x-rays.

“I was attracted to Virginia Tech’s Department of Computer Science and the Sanghani Center because I wanted to conduct impactful research that benefits society and they provided me with the perfect platform to achieve my goals,” Mbakwe.

She said that the outcome of her research is not only applicable in healthcare but could also extend further to other applications in fairness and finance. Last summer she had the opportunity to intern at JPMorgan Chase & Co as an AI research associate and will be returning for a second internship this summer.

Mbakwe earned a bachelor’s degree in mathematics from Nnamdi Azikiwe University, Anambra State, Nigeria, and a master’s degree in computer science and quantitative methods from Austin Peay State University in Clarksville, Tennessee.

Projected to graduate in 2024, she aspires to become a researcher in an industrial research lab and eventually also assume the position of visiting/adjunct professor.


Makanjuola Ogunleye among eight students nationwide to receive Cadence Black Students in Technology Scholarship

Makanjuola Ogunleye is a Ph.D. student in computer science at the Sanghani Center. Photo by Peter Means for Virginia Tech.

Makanjuola Ogunleye, a Ph.D. student in computer science at the Sanghani Center for Artificial Intelligence and Data Analytics, has been awarded a Black Students in Technology Scholarship from Cadence Diversity in Technology Scholarship Programs.

Ogunleye, a member of the Perception and LANguage (PLAN) research lab, is one of eight students pursuing technical degrees at universities across the country who were selected to receive the scholarship based on their impressive academic records, work in the community, leadership potential, and recommendations from professors. He is advised by Ismini Lourentzou, an assistant professor in the Department of Computer Science.  Read full story here.


For chatbots and beyond: Improving lives with data starts with improving machine learning

Ruoxi Jia. Photo by Chelsea Seeber for Virginia Tech.

Assistant Professor Ruoxi Jia in the Bradley Department of Electrical and Computer Engineering and core faculty at the Sanghani Center for Artificial Intelligence and Data Analyitics at Virginia Tech has received an National Science Foundation (NSF) Faculty Early Career Development (CAREER) award to investigate fundamental theories and computational tools needed to measure the value of data. Read full story here.


Sanghani Center Student Spotlight: Shengzhe Xu

Graphic is from the paper “STAN: Synthetic Network Traffic Generation with Generative Neural Models”

Shengzhe Xu chose to pursue a Ph.D. in computer science at Virginia Tech because the Sanghani Center offered him the opportunity to investigate cutting-edge challenges of academic importance and find ways of applying these methodologies to tackle real-world problems.

“What I like best about the center is that everyone is encouraged to pursue their own areas of interest,” said Xu, who is advised by the center’s director, Naren Ramakrishnan. “As students in this free scientific research environment, we just need to concentrate on improving ourselves and conduct in-depth research on the topics we choose.” 

Xu’s work explores semantic analysis of tabular data as well as synthetic tabular data generation. “A real-world example of this is network traffic data,” he said. “Every operation on the Internet is recorded like a footprint that we can model by using deep learning methods.”

But capturing the semantics of tabular data is a challenging problem. Unlike traditional natural language processing and computer vision fields, the overall portrait of tabular data is difficult for humans — even if they are domain experts — to judge because it has complex dependencies that need to explored in depth.

“Deep learning models have achieved great success in recent years but progress in some domains like cybersecurity is stymied due to a paucity of realistic datasets. For privacy reasons, organizations are reluctant to share such data, even internally,” he said. “In order to protect the privacy of training data from being leaked, it is important to explore how to generate good enough tabular data in terms of both training performance and privacy protection.”

Xu presented his work on “STAN: Synthetic Network Traffic Generation with Generative Neural Models” at the MLHat Workshop on Deployable Machine Learning for Security Defense during the 2021 SIGKDD Conference on Knowledge Discovery and Data Mining. The paper explored synthetic data generation in real-world network traffic flow data to protect any sensitive data from data leakage. 

Projected to graduate in 2024, Xu hopes to continue his research as an industry professional.


Sanghani Center Student Spotlight: Afrina Tabassum

Graphic is from the paper “Hard Negative Sampling Strategies for Contrastive Representation Learning”

Afrina Tabassum, a Ph.D. student in computer science, was attracted to the Sanghani Center by the trending research conducted by faculty for improving machine learning algorithms and their application to other fields.

Her research interests lie in machine learning and self-supervised learning, particularly designing novel representation learning objectives for multi-modal data. “I was really attracted to this area of research by an urge to use deep learning in order to make people’s lives easier,” she said.

One of the projects Tabassum is working on at the Sanghani Center is “Hard Negative Sampling Strategies for Contrastive Representation Learning,” a collaboration with her advisors, Hoda Eldardiry and Ismini Lourentzou and a fellow Ph.D. student.

Their paper introduces Uncertainty and Representativeness Mixing (UnReMix) for contrastive training, a method that combines importance scores that capture model uncertainty, representativeness, and anchor similarity. 

“We verify our method on several visual, text and graph benchmark datasets and perform comparisons over strong contrastive baselines,” said Tabassum, “and to the best of our knowledge, we are the first to consider representativeness for hard negative sampling in contrastive learning in a computationally inexpensive way.”

Experimental and qualitative results so far have demonstrated the effectiveness of their proposed approach, she said.

Tabassum is also part of a team from Lourentzou’s PLAN Lab which is competing in the Alexa Prize Taskbot Challenge 2.

“Ten teams across the world were selected to build a taskbot to assist in cooking and performing other tasks around the house. Our bot will be able to make adaptable conversation a reality by allowing customers to follow personalized decisions through the completion of multiple sequential subtasks and adapt to the tools, materials, or ingredients available to the user by proposing appropriate substitutes and alternatives,” she said.

In addition to working on adapting instructions according to the user needs, she is serving as student team leader with responsibilities that include setting clear team goals and short-term deadlines and delegating tasks among all the team members. 

Projected to graduate in 2024, Tabassum would like to pursue a career in industry research.


Dawei Zhou receives Cisco Faculty Research Award to help combat destructive insider threats to cybersecurity

Dawei Zhou

Insider threats to cybersecurity can occur when an actor with authorized access to an organization’s network conducts malicious activities that may release the organization’s critical information that further results in severe consequences such as financial loss, system crashes, and national security challenges.

“These threats are on the rise and according to a recent cyber security survey, 27 percent of cybercrime incidents involved insiders,” said Dawei Zhou, an assistant professor in the Department of Computer Science; director of the VirginiaTech Learning on Graphs (VLOG) Lab and core faculty at the Sanghani Center for Artificial Intelligence and Data Analytics.

One of Zhou’s projects, “Combating Insider Threat: Identification, Monitoring, and Data Augmentation,” targets the challenging problem of how to combat insider threats. He recently received a 2023-2024 Cisco Faculty Research Award that will help support this research.

Zhou said his project uses multiple dynamic and heterogeneous data sources that include internal system logs, employee networks, and email exchange networks.

“Distinctly from other types of terror attacks, insider threats exhibit several unique challenges like  rarity, non-separability, label scarcity, dynamicity, and heterogeneity, making it extremely difficult to catch them in time for a successful counter-attack,” said Zhou. 

He explains: Rarity means that the absolute number of such insiders is extremely small, especially compared with the total number of employees in a large organization or company; non-separability means that the insiders are very good at camouflaging themselves to make them indistinguishable from normal ones and thus able bypass the detection system; label scarcity means that the annotation process of insiders is labor-extensive and time-consuming; dynamicity refers to the time-evolving nature of the raw input data sources as well as the behaviors of insiders; and heterogeneity refers to the heterogeneous data coming from various sources and in various formats.  

“Although different insiders are often conscious and good at camouflaging themselves, they might share some common traits if examined under the proper lens” he said.

With this in mind, the project will try to combat insider threat via an interactive learning mechanism, building new theories and algorithms for the following learning tasks: 

  • Insider Identification: characterize the descriptive and essential properties of insiders and detect groups of insiders – such as traitors, masqueraders, and unintentional perpetrators — with common traits.

  • Insider Monitoring: track the evolution of insider behaviors over time and provide a visual system for analysis, annotation, and diagnosis.

  • Data Augmentation; sanitize input data by completing missing data and cleaning noisy data and generate synthetic insiders to alleviate the label scarcity issue. 

Computer science Ph.D. students Shuaicheng Zhang and Haohui Wang, who are advised by Zhou, will be working with him on the project. A third student, Weije Guan, will be joining the team in the Fall semester.

“We hope that the innovative approach we are taking will result in a better understanding of how to counterattack these threats and ultimately decrease the number of cybercrimes,” Zhou said. 


Virginia Tech researchers receive National Science Foundation award to secure vegetable production in a changing environment

The research team is developing climate-smart, economically efficient, and environmentally sustainable precision agricultural practices that enable more effective and adaptive decision-making as part of our nation’s agricultural priorities. Photo courtesy of USDA.

Virginia Tech researchers in the Center for Advanced Innovation in Agriculture (CAIA) and the Virginia Tech Applied Research Corporation(VT-ARC) were awarded a $750,000 grant by the National Science Foundation Convergence Accelerator program to enhance vegetable production and food security in the commonwealth.

The Sanghani Center for Artificial Intelligence and Data Analytics is a partner on this project. Read full story here.


Lenwood Heath collaborating on plant genome research project funded by National Science Foundation grant

Lenwood Heath

Lenwood Heath, a professor in the Department of Computer Science and core faculty at the Sanghani Center, is part of a team that recently received a National Science Foundation (NSF) grant for its plant genome research project, “Unraveling the origin of vegetative desiccation tolerance in vascular plants collaborators.” Heath is collaborating with colleagues from Texas Tech University and the University of Nevada, Reno on the study.

Excessive water loss is lethal for most plants, but a minority of plants (known as resurrection plants) have a remarkable ability to survive almost complete dryness, said Heath. This ability, known as desiccation tolerance, relies upon a combination of physiological, biochemical, and molecular responses that allow the plant to preserve cell integrity in the dry state.

“In the context of climate change,” Heath said, “we feel it is important to understand how plants respond to drying out and especially important to develop the science that will allow crops to better tolerate drought.”

“It is believed that this resurrection capability depends on genes that are in all plants but lost by most over evolutionary times,” Heath said. “The aim of our project is to discover the essential differences in genetic responses between resurrection plants and drought-sensitive plants so that crops can be re-engineered to be more drought tolerant.” 

In addition to sophisticated biological experiments to measure gene response in the two kinds of plants, the project will employ machine learning techniques, led by Heath, to construct gene regulatory networks (GRNs) for comparative study.  

The grant will provide learning and professional opportunities to graduate students and postdocs at the three universities. Jingyi Zhang, a Ph.D. computer science student advised by Heath, will work with him on the project.

Long-term goals for the project include promoting conservation programs for resurrection species; providing diverse scientific workforce training and outreach activities to first-generation students and the general public; and increasing public awareness about the importance of vegetative desiccation tolerance to future crop breeding in order to tackle the effects of climate change. 


Sanghani Center Student Spotlight: Raquib Bin Yousuf


Graphic is from the paper “Lessons from Deep Learning applied to Scholarly Information Extraction: What Works, What Doesn’t, and Future Directions”

Raquib Bin Yousuf, a Ph.D. student in computer science, is exploring the capabilities of large language models to generate text from different forms of data, especially from knowledge graphs. 

A knowledge graph, he said, can be a network with various entities and their relationships on any domain. Generating the correct and helpful narrative from the knowledge graphs is an important task for the user of that domain. 

“Although my research focus is on natural language processing, I have been fortunate while at the Sanghani Center to work in some other multidisciplinary domains as well,” he said. “The excellent and diverse work of the faculty is what attracted me to the center and the exposure I have had to real-world problems in these collaborative projects has helped me to learn more and conduct better research.”

Yousuf’s first exposure to his research area was through information retrieval projects from large scale text data during his undergraduate years. 

He has also worked on knowledge extraction projects under supervision of his advisor Naren Ramakrishnan, which have involved the application of natural language processing methods on large scale scholarly articles. 

“Recently there has been a pivotal innovation in NLP in the form of the Transformer model and subsequent development of large language models,” Yousuf said. “Today’s large language models can work well, across many tasks, with little to no help at all and that has motivated me to look deep into the working nature of these state of art models for real-world applications.” 

At the 2022 SIGKDD Conference on Knowledge Discovery and Data Mining last August in Washington, D.C., he presented “Lessons from Deep Learning applied to Scholarly Information Extraction: What Works, What Doesn’t, and Future Directions.” The paper explored the use of domain adapted Transformers models as building blocks to develop and deploy an automated End-to-end Research Entity Extractor, capable of extracting technical facets from full-text scholarly research articles of a large scale dataset.

Yousuf received a bachelor’s degree in computer science and engineering from Bangladesh University of Engineering and Technology (BUET) and a master’s degree in computer science from Virginia Tech.

Projected to graduate in 2025, he hopes to continue his research as an industry professional.

 


Danfeng ‘Daphne’ Yao, pioneer and expert in enterprise data security, elevated to IEEE fellow

Danfeng “Daphne” Yao

Danfeng “Daphne” Yao, professor in the Department of Computer Science and affiliate faculty at the Sanghani Center for Artificial Intelligence and Data Analytics at Virginia Tech, has been elevated to fellow, the highest grade of membership in the Institute of Electrical and Electronics Engineers (IEEE), for her contributions to enterprise data security and high-precision vulnerability screening. 

Following a rigorous evaluation procedure, fewer than 0.1 percent of voting members in the institute are selected annually for this career milestone. Read more here.


Sanghani Center Student Spotlight: Hoang Anh Just

Graphic is from the paper “LAVA: Data Valuation Without Pre-Specified Learning Algorithms” 

Hoang Anh Just has received some good news: The paper “LAVA: Data Valuation Without Pre-Specified Learning Algorithms” — on which he is first author — has been accepted as a spotlight at the 11th International Conference on Learning Representations (ICLR) in May. He plans to travel to Rwanda to present the paper. 

Just, a Ph.D. student in the Bradley Department of Electrical and Computer Engineering, said the paper introduces a new perspective on valuating data. 

“For many current valuation methods, the valuation algorithm is based on a model learning process, which is expensive, noise-sensitive, and often impractical. To overcome such hurdles, we valuate data via optimal transport, which requires no model training,” he said. “As such, our data-centric, model-agnostic method effectively detects ‘bad’ data points in the dataset in an efficient manner.”

An interest in artificial intelligence drew him to Virginia Tech and the Sanghani Center. “I am honored to be part of an expanding community that is tackling modern AI problems and pushing the field to greater heights,” Just said.

Just’s advisor, Ruoxi Jia, influenced his research area by introducing him to data evaluation. 

“I really found it intriguing that data are used all around, but we barely know their actual value,” he said, “and this led to my work in establishing efficient and fair methods for valuating data used in machine learning models.”

Just received a bachelor’s degree in computer science and mathematics from Gettysburg College.

Projected to graduate in 2026, his goal is to become a professor who can continue research in data valuation and inspire students to conduct research in artificial intelligence.


Virginia Tech team selected for the Alexa Prize TaskBot Challenge 2 to advance task-oriented conversational artificial intelligence

Ismini Lourentzou (fourth from left) and her team of five computer science Ph.D. students at the Sanghani Center attended a boot camp at Amazon headquarters in Seattle to launch the Alexa Prize TaskBot Challenge 2. The students are (from left) Makanjuola Ogunleye, Muntasir Wahed, Afrina Tabassum, Ismini Lourentzou, Amarachi Mbakwe, and Tianjiao “Joey” Yu.

A Virginia Tech team of  five computer science Ph.D. students at the Sanghani Center for Artificial Intelligence and Data Analytics is one of 10 university teams selected internationally to compete in the Alexa Prize TaskBot Challenge 2. The team will design multimodal task-oriented conversational assistants that help customers complete complex multistep tasks while adapting to resources and tools available to the user, such as ingredients or equipment. Read more here.


Sanghani Center Student Spotlight: Rebecca DeSipio

Graphic is from her research on Parkinson’s Disease

Rebecca DeSipio already knows where she is headed after graduating with a master’s degree in computer engineering this Spring. She will be joining the Charlottesville-based company GA-CCRi, an industry leader in geospatial storage, visualization, and analysis serving government and commercial clients, as a data scientist. 

In looking for a graduate program, DeSipio, who earned a bachelor’s degree in electrical engineering from the Pennsylvania State University, liked the close collaboration between the Bradley Department of Computer and Electrical Engineering and the Department of Computer Science because it allowed her to easily switch from electrical engineering to a computer science specialization, a change she knew she wanted to make.

“And at the Sanghani Center I was introduced to the world of data analytics which has provided me with endless opportunities. Because of my graduate school experience I was able to land a position in that exact area of work. I cannot think how different my career path could have been had I decided to go elsewhere for graduate school,” said DeSipio. 

“I fell in love with Blacksburg and I am beyond excited to stay relatively close and apply all that I have learned here at Virginia Tech to my career,” she said.

When she entered the master’s program, DeSipio — also a Bradley Fellow in the Bradley Department of Electrical and Computer Engineering — discussed research options with her advisor Lynn Abbott. Computer vision and machine learning piqued her interest and she was particularly drawn to biomedical applications for Parkinson’s Disease (PD) because her grandfather had been diagnosed with it. 

“When I came across publications on the use of machine learning algorithms for aiding in the diagnosis of PD by analyzing hand-drawn images, I quickly decided that I wanted to contribute to this line of research,” said DeSipeo.

Currently, she is developing a method that analyzes and rates hand tremor severity in hand-drawn spiral images via frequency features. 

“Since PD is a clinical diagnosis, the goal of my work is to help doctors diagnose and monitor PD progression and find the right medication for their patients,” said DeSipio.

Using her method, if a suspected PD patient goes to the doctor with a hand-tremor, the hand-drawn spiral test can be performed and the tremor rated. Medication can be prescribed and at each follow-up visit, the same spiral test can be performed and rated. 

“My tremor-severity rating system can allow an evaluating doctor to track the progression of the tremor and adjust medications as necessary,” she said. 


Virginia Tech celebrates Innovation Campus construction milestone with Topping Out Ceremony

Construction workers from Whiting-Turner Contracting Co. sign the structural beam that was placed during the Virginia Tech’s topping out ceremony for the Innovation Campus’ Academic Building One on Feb. 7.

More than 275 community members, partners, and friends joined Virginia Tech and Whiting-Turner Contracting Co. on Feb. 7 to celebrate the next milestone for the Virginia Tech Innovation Campus – the topping out ceremony. The event featured a program inside the first floor of the Virginia Tech Innovation Campus construction site and the ceremonial lifting of a steel beam to the highest point – the 11th story – of Academic Building One.

“This is a significant moment for Virginia Tech, symbolizing the tremendous progress we have made on both construction and academic planning for the Innovation Campus,” said Virginia Tech President Tim Sands. “The Innovation Campus will be an important source of tech talent for the greater Washington, D.C., region — and is vital to Virginia Tech’s growing presence in the area. I look forward to 2024, when we welcome students, faculty, and the community into this remarkable building.”

The Innovation Campus will be the Northern Virginia home to the Sanghani Center for Artificial Intelligence and Data Analytics on the fifth floor, a dedicated K-12 Programs Center on the second floor, and the Boeing Center for Veterans and Families, which will be co-located with the Hokie One Stop on the second floor. Read the full story here.


Sanghani Center research takes new approach to analyze depression, anxiety from Reddit posts to provide better care, lower suicide rate

(From left) Chang-Tien Lu with his Ph.D. students Shailik Sarkar, Lulwah AlKulaib, and Abdulaziz Alhamadani. Photo by Joung Min Choi for Virginia Tech.

Suicide, the 10th leading cause of death for adults in the United States and the third leading cause of death among kids ages 10 to 14 and young adults ages 15 to 24, is often the result of an underlying mental health condition such as depression, anxiety, or bipolar disorder. 

Motivated by a suicide mortality by state map released by the Centers for Disease Control and Prevention (CDC) on the increasing severity of mental health crisis — further exacerbated by the COVID-19 pandemic — three Ph.D. students and their advisor at the Sanghani Center for Artificial Intelligence and Data Analytics are analyzing social media in a way that can help social workers and other professionals better understand and tackle different aspects of mental health issues to help prevent suicide. Read the full story here.


Fall rankings spotlight Virginia Tech’s emphasis on research

Graduate students conduct research on test beds at Virginia Tech’s campus in Arlington as part of the Commonwealth Cyber Initiative. Photo by Anthony Wright for Virginia Tech

A strong emphasis on research, a robust commitment to sustainability, and a large international presence among its faculty served as common themes in Virginia Tech’s showing in various national and global rankings this fall.

The university wrapped up the calendar year by being ranked in a tie for No. 265 out of 2,000 universities listed in U.S. News & World Report’s Best Global Universities rankings. The list featured institutions from 95 countries.

Among the university’s 2022 research-related milestones is the partnership between Virginia Tech and Amazon to advance fields of artificial intelligence and machine learning housed in the Virginia Tech College of Engineering and led by the Sanghani Center for Artificial Intelligence and Data Analytics. Read the full story here.


Congratulations to Sanghani Center’s 2022 Summer and Fall Graduates

Virginia Tech’s 2022 Fall Commencement ceremony takes place today.

“We wish our graduates at the Sanghani Center all the best as they receive their Ph.D. and master’s degrees and take the next step toward achieving their career goals,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science at Virginia Tech and director of the Sanghani Center for Artificial Intelligence and Data Analytics

Following are the Sanghani Center’s 2022 summer and fall graduates:

Ph.D.

Nikhil Muralidhar, advised by Naren Ramakrishnan and Anuj Karpatne, has earned a Ph.D. in computer science. His research interest is in leveraging machine learning to address problems in scientific applications leveraging data and scientific theory. He also received a graduate certificate in Urban Computing. The title of his dissertation is “Science Guided Machine Learning: Incorporating Scientific Domain Knowledge for Learning Under Data Paucity and Noisy Contexts.”  In Fall 2022, Muralidhar joined the Computer Science Department at Stevens Institute of Technology in Hoboken, New Jersey, as an assistant professor and leads the Scientific Artificial Intelligence (ScAI) Lab to develop scientific machine learning solutions incorporating data and domain knowledge in physics, fluid dynamics, cyber-physical systems and disease modeling.

Xinyue Wang, advised by Edward Fox, has earned a Ph.D. in computer science. His research focuses on web archive processing and analysis infrastructure through distributed computation. The title of his dissertation is “Large Web Archive Collection Infrastructure and Services.” Wang is joining Yahoo in San Jose, California, as a research scientist.

Master’s degree

Huiman Han, advised by Chris North, has earned a master’s degree in computer science. Her research focuses on visual analytics, interactive machine learning, and explainable artificial intelligence. The title of her thesis is “Explainable Interactive Projections for Image Data.” Huimin is joining LinkedIn in Mountain View, California, as a software engineer in Machine Learning.

Sarah Maxseiner, advised by Lynn Abbott, received a master’s degree in electrical and computer engineering. Her thesis is on assessing the quality level of hand-drawn sketches.  


Beat the heat: Virginia Tech team to study best ways to survive heat waves

Theo Lim, an assistant professor of urban affairs and planning at Virginia Tech, is leading a research team studying ways that people can survive, adapt, and thrive through high temperatures and heat waves.

Each year, heat waves kill more Americans than any other natural disaster. Climate change has exacerbated the problem by creating measurably higher temperatures in areas of cities with fewer economic and social resources to mitigate the impacts of heat, according to Theo Lim, an assistant professor of urban affairs and planning at Virginia Tech.
 
Lim is leading a multidisciplinary Virginia Tech research team that is partnering with the City of Roanoke to help residents survive, adapt, and thrive through high temperatures and heat waves.


The six-person team received a Stage 1 Civic Innovation Challenge (CIVIC) Planning Grant, funded by the National Science Foundation. The challenge supports rapid implementation of community-driven, research-based pilot projects that address heat resilience priorities.


Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering at Virginia Tech and director of the director of the Sanghani Center for Artificial Intelligence and Data Analytics, is one of the co-investigators of the project.  Read full story here.


Virginia Tech HokieBot competes in Alexa Prize SocialBot Grand Challenge 5 to develop advanced AI technology that enhances user conversation

(From left) Lifu Huang, assistant professor of computer science and faculty at the Sanghani Center is advising a team of Ph.D. students — Minqian Liu, Ying Shen, Zhiyang Xu, and Barry Yao — competing in the Alexa Prize SocialBot Grand Challenge 5 sponsored by Amazon.com Services LLC. Photo by Jingyuan Qi.

A team of four graduate students at the Sanghani Center for Artificial Intelligence and Data Analytics is one of nine international university teams selected to compete in the Alexa Prize SocialBot Grand Challenge 5 sponsored by Amazon.com Services LLC. Each participating team will receive up to a $250,000 research grant to build a skill that can help Alexa converse with users on popular topics and current events for at least 20 minutes while achieving a user rating of at least 4.0/5.0. Top finishing teams will also be eligible for various prizes. Read more here.


Amazon Fellows and faculty-led projects advance innovations in machine learning and artificial intelligence

Amazon Fellows are Ph.D. students (from left) Qing Guo and Yi Zeng. Photos courtesy of the fellows.

The Amazon–Virginia Tech Initiative for Efficient and Robust Machine Learning has announced support for two Amazon Fellows and four innovative research projects led by Virginia Tech faculty that further the initiative’s mission of advancing new directions in machine learning.

Funded by Amazon, housed in the College of Engineering, and directed by researchers at the Sanghani Center for Artificial Intelligence and Data Analytics on Virginia Tech’s campus in Blacksburg and at the Innovation Campus in Alexandria, the initiative was launched in March to support student and faculty-led development and implementation of innovative approaches to robust machine learning — such as ensuring that algorithms and models are resistant to errors and adversaries — that could address worldwide industry-focused problems. Read more here.


Sanghani Center Student Spotlight: Han Liu

Graphic is from the paper, “Case Study Comparison of Computational Notebook Platforms for Interactive Visual Analytics”

Having earned a bachelor of science degree in computer science at Virginia Tech in 2020, Han Liu remained at the university to continue his education and pursue a master’s degree. 

“On a personal note, I love the beautiful surroundings in Blacksburg,” said Liu. “But more importantly, my decision was influenced by the professors I have met here who are passionate about their fields and actively support students in their studies and research.” 

Liu, who is advised by Chris North, added that “as a graduate student, the Sanghani Center has provided me with many exciting academic and research opportunities.”

Liu’s research focuses on visual analytics and how to represent data in a way that helps users understand and interpret it more easily.

“Existing notebook platforms have different capabilities for supporting the use of visual analytics and it is not clear which platform to choose for implementing visual analytics notebooks,” said Liu. “My work explores how to best implement these notebooks to solve problems, particularly in data science scenarios.” 

On October 16, Liu will present a short paper, “Case Study Comparison of Computational Notebook Platforms for Interactive Visual Analytics,” at the Visualization in Data Science (VDS) Symposium associated with IEEE VIS 2022 in Oklahoma City.

He is on track to graduate this semester (FALL 2022) and his ultimate goal “is to become a data scientist who can analyze data for actionable insights and help people by solving real-world problems.”


Sanghani Center Student Spotlight: Mia Taylor

Graphic is from the paper: “Andromeda in the Classroom: Collaborative Data Analysis for 8th Grade Engineering Design”

Mia Taylor began her freshman year at Virginia Tech as a five-year accelerated bachelor of science/master of science program in computer science. She will graduate this semester (FALL 2022) and has already accepted a position of research engineer on the machine learning team at Graf Research in Blacksburg.

Her research at the Sanghani Center has focused on how students use the interactive dimensionality reduction application Andromeda. “I want to understand how students — when given complex data analysis tools — learn from the experience of conducting exploratory data analysis,” said Taylor, who is advised by Chris North.

Taylor’s collaborative full paper, “Andromeda in the Classroom: Collaborative Data Analysis for 8th Grade Engineering Design,” was published by the 2022 American Society for Engineering Education (ASEE) Annual Conference and Exposition in June.

For this study, the classroom teacher uploaded data describing projects to Andromeda with each point in the visualization representing a student’s design. With Andromeda controlled by the teacher, students used it to visualize, analyze, and compare their designs in extended conversation with each other and the teacher and collectively explore their design-related data.

““Despite not having the mathematical background to understand dimensionality reduction, the students in our study learned about relations between variables and felt that Andromeda helped them compare their designs in a friendly, but competitive manner,” Taylor said. 

In the study, she said, the team also suggested ways of improving Andromeda’s utility as a public, educational resource and provided an example of class activity aligned with Virginia’s proposed Standards of Learning in data science. 

Taylor was introduced to research in visualization while doing an undergraduate capstone project in human-computer interaction. 

“The Sanghani Center conducts interesting research within data science and machine learning,” said Taylor, “and as a master’s degree student, it has afforded me useful connections across the field which will continue to be valuable as I will be remaining in the Blacksburg area as a machine learning engineer after graduation.”


Professional summer internships provide valuable real-world experience for Sanghani Center graduate students

Huimin Han, a master’s degree student in computer science, is spending the summer in Sunnyvale, California, for an internship at LinkedIn

Graduate students at the Sanghani Center often embark on summer internships to gain real-world experience and in some instances, enable them to also advance their own research interests and projects. Summer 2022 is no exception. While some companies and research labs are continuing to operate remotely, a number of students have returned to working on-site.

Following is a list of Sanghani Center interns, where they are working, and what they are doing:

Sikiru Adewale, a Ph.D. student in computer science, is a graduate technical research intern at Intel Corporation, working remotely. He is using machine learning to analyze the workloads dataset. His advisor is Ismini Lourentzou.

Jie Bu, a Ph.D student in computer science, is a machine learning research intern for an Apple Maps Team in Cupertino, California, working on-site. He is helping to optimize user experience and map services using deep learning methods. His advisor is Anuj Karpatne.

Satvik Chekuri, a Ph.D. student in computer science, is an natural language processing intern with the Deloitte Audit and Assurance Data Science team in New York City, working remotely on the entity extraction and entity linking problem for unstructured data in the financial domain. His advisor is Edward Fox.

Nurendra Choudharya Ph.D. student in computer science, is an applied science intern at Amazon A9 in Palo Alto, California, working on-site on use case of graph and language representation in the context of e-commerce platforms. His advisor is Chandan Reddy.

Elizabeth Christmana master’s degree student in computer science, is a software engineering intern at Splunk in Blacksburg, Virginia, working remotely on automating the build process for Stream, a Splunk add-on for deep packet inspection. Her advisor is Chris North.

Arka Daw, a Ph.D. student in computer science, is a research intern at IBM T.J. Watson Research Center in New York, working on-site. He is developing physics-informed AI methods to solve inverse problems involving partial differential equations. His advisor is Anuj Karpatne.

Mohannad Elhamod, a Ph.D student in computer science, is an intern at NASA Langley Research Center in Hampton, Virginia, working remotely on applying machine learning in material engineering. His advisor is Anuj Karpatne.

Jiaying Gong, a Ph.D. student in computer science, is a research scientist intern at Rakuten in Boston, Massachusetts, working remotely on multi-label few-shot learning in natural language processing. Her advisor is Hoda Eldardiry.

Naveen Guptaa master’s degree student in computer science, is a software engineering intern at Kentik in San Francisco, California, working remotely in the web development domain and using React, Node JS, and Express JS in building SAAS products. His advisor is Anuj Karpatne. 

Huimin Han, a master’s degree student in computer science, is a machine learning engineer intern at LinkedIn in Sunnyvale, California, working on-site. She is exploring machine learning techniques to build the most accurate occupational taxonomy for every Linkedin member. Her advisor is Chris North.

Jianfeng He, a Ph.D. student in computer science, is an applied scientist intern on the AWS AI team at Amazon in Seattle, Washington, working on-site. He is doing research related to audio, text, and semantic understanding. His advisor is Chang-Tien Lu.

Meghana Hollaa master’s degree student in computer science, is a machine learning intern on the Data Technologies team at Bloomberg LP in New York, working on-site. She is researching and optimizing entity extraction methodologies on financial documents with emphasis on low inference times. Her advisor is Ismini Lourentzou.

Aneesh Jain, a master’s degree student in computer science, is a machine learning engineering intern at Cadence Solutions, working remotely on applications of language models in the healthcare domain. His advisor is Chandan Reddy.

Gaurang Karwande, a master’s degree student in the Bradley Department of Electrical and Computer Engineering, is a machine learning intern at VideaHealth, Inc., in Boston, Massachusetts, working on-site in the field of medical imaging and developing AI-powered solutions in dentistry. His advisor is Ismini Lourentzou. 

Yoonjin Kim, a Ph. D. student in computer science, is a graduate software research intern at Intel IP and Competitive Analysis in Santa Clara, California, working an on-site/virtual hybrid. She is gaining industry exposure to the latest trend in workloads and workload-related research. Her advisor is Lenwood Heath. 

M. Marufa Ph.D. student in computer science, is an applied scientist intern at Amazon.com in Seattle, Washington, working on-site to solve an image referencing problem with a goal of improving Amazon delivery experiences. His advisor is Anuj Karpatne.

Amarachi Blessing Mbakwea Ph.D. student in computer science, is an AI research associate at JPMorgan Chase & Co in New York City, working on-site. She is conducting research on natural language processing-related problems. Her advisor is Ismini Lourentzou.

Makanjuola Ogunleye, a Ph.D student in computer science, is a data scientist intern at Intuit, working remotely with the AI Capital team in Mountain View, California. The team is building a natural language processing and AI framework to improve the company’s risk assessment strategy and policy that will be added as a reusable service to the Intuit AI core capital group. His advisor is Ismini Lourentzou.

Medha Sawhneya master’s degree student in computer science, is a machine learning engineering intern at Twitter in San Francisco, California, working remotely with the Health ML Team. Her advisor is Anuj Karpatne. 

Avi Seth, a master’s degree student in computer science, is a data scientist intern at Gastrograph AI in New York City, working remotely on generalizing the preference prediction model for flavor profiles across different demographics. His advisor is Ismini Lourentzou. 

Afrina Tabassum, a Ph.D. student in computer science, is an intern at Los Alamos National Laboratory (LANL) in New Mexico, working remotely. She is exploring machine learning techniques under varying data quality. Her advisor is Hoda Eldardiry.

Mia Taylor, a master’s degree student in computer science, is a graduate research engineer intern at Graf Research in Blacksburg, Virginia, working on-site. She is conducting applied machine learning research in a hardware context. Her advisor is Chris North.

Muntasir Wahed, a Ph.D. student in computer science, is a research intern at IBM Research – Almaden in San Jose, California, working on-site with the Intelligence Augmentation Group on set expansion techniques to build lexicons for natural language processing tasks. His advisor is Ismini Lourentzou.

Sijia Wang, a Ph.D. student in computer science, is an applied science intern at Amazon Web Services in New York City, working on-site on information extraction, entity linking, and related natural language processing tasks. Her advisor is Lifu Huang. 

Xinyue Wang, a Ph.D. student in computer science, is a research intern on the Media Science Team at Yahoo, in San Jose, California, working on-site on a project related to trending user search queries and term refinement. His advisor is Edward Fox.

Zhiyang Xu, a Ph.D. student in computer science, is an applied scientist intern at Amazon Alexa AI in Sunnyvale, California, working on-site to detect the inconsistency of facts in dialog systems and improve the interpretability of the detecting process. His advisor is Lifu Huang.

Yi Zeng, a Ph.D. student in computer engineering, is an AI research intern at SONY Corporation of America in New York City, working remotely on developing a meta-learning-based method against general training data corruptions from a security perspective. His co-advisor is Ruoxi Jia.


Six faculty win seed funding for new projects

Photo by Peter Means for Virginia Tech.

Ed Fox, a professor of computer science and faculty member at the Sanghani Center for Artificial Intelligence and Data Analytics, is part of a research team tackling pressing questions about how global change will affect transmission of infectious disease between species, beginning with how rabies moves from vampire bats to other animals. Click here to read more.


Innovation Campus faculty, curriculum, and building continue to take shape

Virginia Tech broke ground on Academic Building One of the Innovation Campus in Alexandria in September. The campus’ first building will be 300,000 square feet and 11 stories tall when completed in 2024.

At his June 6 update to the Virginia Tech Board of Visitors, Lance Collins, vice president and executive director of the Virginia Tech Innovation Campus, emphasized the appointment of 12 computer science and computer engineering faculty members to the campus team.

Collins further detailed the “faculty lead” positions, with several of the new faculty members accepting key leadership roles at the institution. 

Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the Sanghani Center for Artificial Intelligence and Data Analytics, will serve as faculty lead for artificial intelligence and applied machine learning at the Innovation Campus. Read more here


Sanghani Center Student Spotlight: Hongjie Chen

Graphic is from the paper “Graph Deep Factors for Forecasting with Applications to Cloud Resource Allocation”

Hongjie Chen’s Ph.D. research in computer science lies in the areas of graph neural networks, time-series analysis, and recommendation systems. 

“More specifically, I am currently working on time-series forecasting which is really useful in everyday life,” Chen said. “I am targeting accurate workload prediction in Cloud computing nodes.”

He said he was drawn to the Sanghani Center for its exciting advanced research atmosphere and excellent teaching faculty. He is advised by Hoda Eldardiry

In August 2021 he presented collaborative work with researchers at Adobe Research (where he interned the summer before) and Eldardiry in proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD). Their paper, “Graph Deep Factors for Forecasting with Applications to Cloud Resource Allocation,” proposes a relational global model that learns complex non-linear time-series patterns globally using the structure of the graph to improve both forecasting accuracy and computational efficiency and that not only considers its individual time-series but also the time-series of nodes that are connected in the graph. 

The experiments, Chen said, demonstrate the effectiveness of the proposed deep hybrid graph-based forecasting model compared to the state-of-the-art methods in terms of forecasting accuracy, runtime, and scalability,” 

“Our case study reveals that GraphDF can successfully generate cloud usage forecasts and opportunistically schedule workloads to increase cloud cluster utilization by 47.5 percent on average,” he said.

Another collaborative paper, “Context Integrated Relational Spatio-Temporal Resource Forecasting,” was published at the 2021 IEEE International Conference on Big Data.

Chen earned a bachelor’s degree in computer science from Xiamen University, China. He is projected to graduate in 2024 and would like to continue working in the field of time-series analysis.


Sanghani Center Student Spotlight: Sijia Wang

Graphic is from the paper “Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding”

The Spring 2022 semester was a memorable one for Ph.D. student Sijia Wang.

The Association for Computational Linguistics (ACL) accepted the paper, “Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding,” which she is presenting on May 24 during its international meeting in Dublin. 

And she is part of the Virginia Tech team from the Sanghani Center that is one of 10 finalists chosen to compete in the Alexa Prize SimBot Challenge. The challenge focuses on advancing the development of next-generation virtual assistants that continuously learn and gain the ability to perform common sense reasoning to help humans complete real-world tasks. 

Wang’s specific role on the team — advised by Lifu Huang, who is also her academic advisor — is to establish knowledge graphs from instructional articles, images, and video demonstrations on the internet, such as WikiHow. She will also concentrate on collectively grounding entities and actions extracted from text to video to associate each entity or action with a visual image or video clip.

In her research, Wang focuses on natural language processing and machine learning, particularly  information extraction with full or limited supervision. 

Information extraction, she said, poses challenges because of its sophisticated annotation needs and variance benchmarks, she said. 

“I am trying to automatically extract structured information from unstructured data,” Wang said. “For example, in the sentence ‘Melony was married just a month before she left for Iraq,’ the word ‘she’ indicates Melony, and her marriage occurs before the movement event. My research focus is to extract this information from the input sentence.”

Wang said that as a young child she wanted to understand foreign languages but knew that it would take a great effort to do so. “When I learned about machine learning as an undergraduate student, I was really drawn to it because of how we can use its model fitting and pattern learning abilities to automatically understand visual content.”  

Wang holds a bachelor’s degree in vehicle engineering from Southwest Jiaotong University in China and a master’s degree in computer science from Washington University in St. Louis. She was drawn to Virginia Tech and the Sanghani Center for a Ph.D. computer science program because of the experienced professors and their cutting-edge research in artificial intelligence and data science. “Their work and achievements and all the passionate students around me have motivated me to work harder,” she said.

Being a Ph.D. student has made her realize how much time and effort it takes to become a successful academic researcher, she said. “So after graduation, I will be looking for a postdoc position or other research opportunities at private and research labs to become better equipped to become a research scientist.”

Wang is projected to graduate in 2024.


Virginia Tech alumna named ethics co-chair for leading conference in artificial intelligence and machine learning

Cherie Poland earned a Master of Engineering from the Department of Computer Science’s first new graduate program for the Virginia Tech Innovation Campus. Photo courtesy of Cherie Poland

Virginia Tech alumna Cherie Poland has been named one of four ethics co-chairs for the 36th Conference on Neural Information Processing Systems (NeurIPS), the most prestigious conference in artificial intelligence and machine learning (AI/ML).  

This is the latest entry on Poland’s list of achievements. To name only a few, she was issued one European patent and had five of her U.S. patent applications filed; created and later sold a biotech company; ran her family-owned cattle ranch (while holding a full-time position as a biotech patent examiner at the U.S. Patent and Trademark Office); and earned six college degrees, including a J.D.

The most recent, in December 2021, is from Virginia Tech, where she was a student at the Sanghani Center for Artificial Intelligence and Data Analytics. She received a Master of Engineering from the Department of Computer Science, one of the first degree programs for the university’s Innovation Campus. Read more about Poland here.


Congratulations to Sanghani Center Spring 2022 Graduates

Spring 2022 Commencement ceremonies and related events are under way on Virginia Tech campuses in Blacksburg and in the greater metropolitan D.C. area. 

“We celebrate our graduates who have persevered over hurdles raised by the Covid pandemic to reach their academic goals. For longer than anyone would have suspected at the onset of the pandemic, this group of students had to adapt to a virtual environment. Online, they attended classes, met with their advisors, conducted research, presented papers at conferences, and worked at internships,” said  Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science at Virginia Tech and director of the Sanghani Center for Artificial Intelligence and Data Analytics. “We are proud of all they have accomplished during their years at the center and wish them continued success as they begin their professional careers.”

Following is a list of Sanghani Center graduates:

Ph.D.

Chidubem Arachie, advised by Bert Huang, has earned a Ph.D. in computer science. His research interest lies in developing algorithms for weakly supervised learning. The title of his dissertation is “Learning with Constraint-Based Weak Supervision.” Arachie is joining Google in California as a software engineer.

Yali Bian, advised by Chris North, has earned a Ph.D. in computer science. His research interests include human-computer interaction, visual analytics, machine learning, and machine teaching. The title of his dissertation is “Human-AI Sensemaking with Semantic Interaction and Deep Learning.” Bian is joining the Human and AI Systems Research (HAR) Lab at Intel Labs, Santa Clara, California, as a research scientist. 


Subhodip Biswas, advised by Naren Ramakrishnan, has earned a Ph.D. in computer science. His primary research lies in spatial data mining, geographic information systems, education, and crowdsourcing. The title of his dissertation is “Spatial Optimization Techniques for Redistricting.” He has also earned a graduate certificate in urban computing. Biswas is joining the AI verification team at the autonomous vehicle company Zoox in Foster City, California.

Debanjan Datta, advised by Naren Ramakrishnan, has earned a Ph.D. in computer science. Datta’s research focus is on data mining and machine learning, with a special interest in algorithms on anomaly detection and tabular data. The title of his dissertation is “A Framework for Automated Discovery and Analysis of Suspicious Trade Records.” Datta is joining Amazon Web Services (AWS) as an applied scientist.

Chen Gao, advised by Jia-Bin Huang, has earned a Ph.D. in electrical and computer engineering. His research interest lies in the field of computational photography and computer vision. He is focusing on view synthesis and video manipulation. The title of his dissertation is “Learning Consistent Visual Synthesis.” Chen will be joining Meta in Seattle, Washington, as a research scientist.

Taoran Ji, advised by Chang-Tien Lu, has earned a Ph.D. in computer science. His research interests include natural language processing, text mining, and machine learning. The title of his dissertation is “On Modeling Dependency Dynamics of Sequential Data: Methods and Applications.” Ji has joined Moody’s Analytics in New York, as director, artificial Intelligence and machine learning. 

Xiaolong Li, advised by Lynn Abbott, has earned a Ph.D. in electrical and computer engineering. His primary research interest is in the area of computer vision, with a special focus on deep 3D representations learning toward dynamic scene understanding. The title of his dissertation is “3D Deep Learning for Object-Centric Geometric Perception.” Li is joining AWS AI in Seattle, Washington, as an applied scientist.

Yuliang Zou, advised by Jia-Bin Huang, has earned a Ph.D. in electrical and computer engineering. His research interest lies in designing label-efficient and/or robust visual understanding methods. The title of his dissertation is “Label-Efficient Visual Understanding with Consistency Constraints.” Zou is joining Waymo, an autonomous driving technology company in Mountain View, California, as a research scientist.

Master’s Degree

Larissa Basso, advised by Chang-Tien Lu, has earned a master’s degree in computer science. Her primary research focus is satellite image retrieval. The title of her thesis is “CLIP-RS: A Cross-modal Remote Sensing Image Retrieval Based on CLIP, Northern Virginia Case Study.” 

Chih-Fang Chen, advised by Chang-Tien Lu, has earned a master’s degree in computer science. His primary research interest is urban computing. The title of  his thesis is “Metrohelper: A Real-time Web-based System for Metro Incidents Detection Using Social Media.” Chen is joining Amazon as a software developer engineer.

Kai-Hsiang Cheng, advised by Chang-Tien Lu, has earned a master’s degree in computer science. His primary research interests are applied machine learning and data mining. The title of  his thesis is “Leverage Fusion of Sentiment Features and Bert-based Approach to Improve Hate Speech Detection.” Cheng is joining Gettr in New York City as software developer.

Riya Daniadvised by Ismini Lourentzou, has earned a master’s degree in computer science. Her primary research involves generating videos of unseen concepts using machine learning. The title of her thesis is “Concept Vectors for Zero-Shot Video Generation.” Dani is joining Amazon Web Services (AWS) in Northern Virginia as an associate solutions architect.

Xuan Li, advised by Lynn Abbott, has earned a master’s degree in electrical and computer engineering. His research focuses on continual learning that prevents a deep neural model from catastrophic forgetting in sequential tasks. The title of his thesis is “Referencing Unlabelled World Data to Prevent Catastrophic Forgetting in Class-incremental Learning.” Li is joining Amazon as software development engineer.

Gopikrishna Rathinavel, advised by Naren Ramakrishnan, has earned a master’s degree in computer science. His research focus is on using deep learning techniques for wireless anomaly detection. The title of his thesis is “Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback.”

Stephen Sun, advised by Chang-Tien Lu, has earned a master’s degree in computer science. His primary research interest is social media analytics. The title of his thesis is “Estimate Flood Damage Using Satellite Images and Twitter Data.” Sun is joining TikTok Inc. in Mountain View, California, as a software engineer.

Han Xu, advised by Lynn Abbott, has earned a master’s degree in electrical and computer engineering. His research focuses on skin segmentation without color information. The title of his thesis is “Color Invariant Skin Segmentation.” 


Mehul Sanghani to deliver College of Engineering commencement address as Distinguished Alumni Speaker

Mehul Sanghani. Photo courtesy of Chuck Kennedy.

As founder and CEO of Octo, a technology and consulting firm focused on using emerging technologies to solve the federal government’s most challenging problems, Mehul Sanghani ’98 is no stranger to opportunity, sacrifice, and even regret. He’ll impart these messages and others to graduates of Virginia Tech’s College of Engineering as the Distinguished Alumni Speaker for the spring 2022 commencement ceremonies on May 14. Read more about his life and philanthropy — including a generous endowment for the Sanghani Center for Artificial Intelligence and Data Analytics here.


Twelve Virginia Tech faculty join Innovation Campus

Twelve highly accomplished Virginia Tech faculty experts in computer science and computer engineering have formally affiliated with the Virginia Tech Innovation Campus in Alexandria. This first cohort of Innovation Campus faculty will play a vital role in shaping the new campus by helping to establish key research themes, enhancing the project-based curriculum, and developing the campus governance structure. Among these 12 experts are Naren Ramakrishnan, Thomas L. Phillips Professor of Engineering and director of the Sanghani Center for Artificial Intelligence and Data Analytics and center faculty Chang-Tien Lu, professor of computer science and director of the computer science program, Northern Virginia. Read more here.


Sanghani Center Student Spotlight: Mandar Sharma

Graphic is from the paper “T3: Domain-Agnostic Neural Time-series Narration”

Would you like a virtual assistant that could go through chunks of large reports with pages upon pages of tables and raw numeric data and summarize it all in a short paragraph? 

This is what Mandar Sharma is trying to accomplish with his Ph.D. research in the area of natural language generation.

“The progress of artificial intelligence depends heavily upon our ability to communicate with machines and natural language is the crux of human communication,” Sharma said. The paper, “T3: Domain-Agnostic Neural Time-series Narration,” which he presented at the 2021 IEEE International Conference for Data Mining, generates succinct narratives that describe large time-series datasets.

“With a dataset of time-series and narrative pairs, a promising direction for future exploration lies in learning direct mappings from numbers to text, extending beyond just time-series,” said Sharma, who is advised by Naren Ramakrishnan.

Another paper relating to his research, “Once Upon A Time In Visualization: Understanding the Use of Textual Narratives for Causality,” was published at the 2020 IEEE Transactions on Visualization and Computer Graphics.

Sharma has an undergraduate degree in electrical engineering from the Institute of Engineering, Tribhuvan University, Nepal, where he achieved the highest rank in his department. He dabbled with machine learning in his undergrad thesis, he said, when his team used a Haar cascade classifier to train a robot to follow human gestures. 

Post-graduation, he worked in software development for a while but found it unrewarding. So he joined his alma mater as a research assistant and there began exploring the field of natural language processing. 

The decision to pursue research as a Ph.D. student in computer science led him to Virginia Tech. “Dr. Ramakrishnan’s strong theoretical background and openness to trying novel and diverse areas of machine learning brought me to the Sanghani Center. And I really appreciate his understanding and amenable nature as an advisor.”

Sharma said the Sanghani Center is particularly appealing to him because it integrates multiple facets of machine learning research into one collaborative environment. 

Sharma is projected to graduate in the 2023-24 academic year.

“The perfect life for me post-graduation would be a full-time position as an industrial researcher with a part-time affiliation at a nearby university where I can teach machine learning but we will see what the future brings,” he said.


CI Fellow Rebecca Faust brings expertise on human-AI interaction methods for dimension reduction to Sanghani Center work with Chris North

Rebecca Faust

A 2021 Computing Innovation (CI) Fellow, postdoc Rebecca Faust, has been working with Chris North, professor in computer science and associate director at the Sanghani Center for Artificial Intelligence and Data Analytics, since January.

They are exploring how to create explanations of the effects of semantic interactions on a deep learning model through the analysis of perturbations and differences in the model after interactions. 

“Through these explanations, we hope to demonstrate how models adjust when people inject prior knowledge into them through semantic interaction and validate whether the updated model adequately captures this prior knowledge,” said Faust, who earned her Ph.D. in computer science from the University of Arizona in December 2021. 

Faust will also help lead Department of Defense (DoD)-funded projects on interactive analytics, funded through the Center for Space, High-Performance, and Resilient Computing (SHREC).

“Dr. North was at the top of my list,” she said. “Together, we crafted an application, including a research proposal, a fellowship plan, and a mentorship plan, and submitted it to the program.” 

“Becca is well-known in the research field for her dissertation work on human-AI interaction methods for dimension reduction — helping people understand relationships in high-dimensional data,” said North. “She joins us at the Sanghani Center to lead research efforts on interactive explainable deep learning with neural networks. Ultimately, this work will enable human interactive sensemaking tasks in more complex forms of data such as collections of textual documents, images, or videos.” 

Faust’s dissertation, “A Visualization First Perspective on Program Understanding,” can be accessed here.

Her long-term goal is to become a tenure-track faculty member at a major research university, where she can work to promote diversity in training up the next generation of computer scientists.

“My postdoc position at Virginia Tech will help me develop the skills necessary to succeed as a faculty member, including mentoring students, grant writing, and network building,” said Faust. “It also provides the opportunity to establish a stronger research presence and create a research plan for the coming years.”


Virginia Tech and Amazon establish machine learning research partnership

January 21, 2020 – Students and faculty of the Data Analytics Center work together at the Virginia Tech Research Center – Arlington. (Photo by Erin Williams/Virginia Tech)

Virginia Tech and Amazon are partnering to advance research and innovation in artificial intelligence and machine learning. The Amazon – Virginia Tech Initiative for Efficient and Robust Machine Learning will include machine learning-focused research projects, doctoral student fellowships, community outreach, and an establishment of a shared advisory board.

“This partnership affirms the value of our connection to Amazon as we scale up project-based learning and research programs in artificial intelligence and machine learning,” said Virginia Tech President Tim Sands. “Building Virginia Tech’s strength and expertise in these fields will support critical technological advancements and our commitment to fuel workforce development in the commonwealth.” 

“We are delighted to collaborate with Virginia Tech in launching this new initiative which brings together the top talent in our two organizations in a joint mission to achieve ground-breaking advances in robust machine learning,” said Prem Natarajan, vice president of Alexa AI – Natural Understanding at Amazon. “The proximity of this initiative to Amazon’s HQ2 will catalyze research efforts that leverage the depth of talent in the Northern Virginia area to address some of the most pressing challenges in AI.”  To learn more about the initiative which will be housed in the College of Engineering and led by Sanghani Center for Artificial Intelligence and Data Analytics researchers, read full story here.


Scientists partner on multi-university grant to establish a field of ‘imageomics’

The Imageomics Institute will create a new field of study that uses images of living organisms to understand biological life processes.

Researchers in three different disciplines at Virginia Tech are partnering in a $15 million grant from the National Science Foundation (NSF) to establish an institute in the new field of “imageomics,” aimed at creating a new frontier of biological information using vast stores of existing image data, such as publicly funded digital collections from national centers, field stations, museums, and individual laboratories. 

The goal of the institute is to characterize and discover patterns or biological traits of organisms from images and gain insights into how function follows form in all areas of biology. It will expand public understanding of the rules of life on Earth and how life evolves.

Imageomics is one of five Harnessing the Data Revolution institutes receiving support from the NSF.  

Anuj Karpatne, assistant professor in the Department of Computer Science and faculty at the Sanghani Center for Artificial Intelligence and Data Analytics, is serving as one of four co-investigators for the multi-university project led by the Ohio State University. Leanna House, associate professor in the Department of Statistics and faculty at the Sanghani Center, and Josef Uyeda, assistant professor in the Department of Biological Sciences, are designated senior personnel. All three researchers are part of the executive leadership team of the institute and investigators on Virginia Tech’s $1.4 million portion of the grant. Click here to read more about these scientists will apply their expertise to the project.


Virginia Tech team selected as finalist in Alexa Prize SimBot Challenge to advance next-generation virtual assistants

One of 10 finalists in the Alexa Prize SimBot Challenge, Virginia Tech’s team members meet regularly for updates on their specific work and overall progress on the project. The winner will be announced in 2023. Photo by Andrew Cybak for Virginia Tech.

A Virginia Tech team from the Sanghani Center for Artificial Intelligence and Data Analytics is one of 10 finalists chosen to compete in the Alexa Prize SimBot Challenge. The challenge focuses on advancing the development of next-generation virtual assistants that continuously learn and gain the ability to perform common sense reasoning to help humans complete real-world tasks.

“The SimBot should be able to understand the intention of a task as well as any instructions or feedback it receives from a user and interpret the environment to correctly predict what action is needed to complete it,” said Lifu Huang, assistant professor of computer science and faculty at the Sanghani Center.  Click here to read more about how the team will tackle this challenge.



Sanghani Center Student Spotlight: Kylie Davidson

Graphic is from the paper “Sensemaking Strategies with Immersive Space to Think” 

Focused on using virtual/augmented reality for day-to-day productivity tasks, Kylie Davidson is investigating how immersive technologies can be used during sensemaking. 

“The goal is to add computational analytics to our software prototype to assist the user in real-time while they complete a sensemaking task,” she said.

After graduating with a bachelor’s degree in computer science from James Madison University, Davidson chose a Ph.D. program at Virginia Tech where she could conduct cutting-edge computer science research with real-world impact.”

“At the Sanghani Center,” she said, “I get to work with a community of researchers who are solving real-world problems every day.”  

Davidson is co-advised by Chris North at the Sanghani Center and Doug Bowman, director of the Center for Human-Computer Interaction. She said that while she has always had an interest in virtual and augmented reality, her advisors were instrumental in helping her find a way to research the use of these technologies for real-world tasks.

North and Bowman were among her collaborators on the paper, “Sensemaking Strategies with Immersive Space to Think,” presented at the 2021 IEEE Virtual Reality and 3D User Interfaces conference.

She and North also collaborated with other researchers on Traces of Time through Space: Advantages of Creating Complex Canvases in Collaborative Meetings,” published in proceedings of the ACM on Human Computer Interaction, November 2021.

Davidson is also part of the New Horizons Graduate Scholars community at Virginia Tech, a collaborative research network of ambitious engineering graduate students who are nominated by their departments and provided with resources and opportunities that can strengthen their academic career while at Virginia Tech as well as prepare for a successful future.

Her projected graduation date is Spring 2024.


Ismini Lourentzou awarded NSF grant to develop infrastructure for more effective AI in U.S. manufacturing industry

Ismini Lourentzou

Because artificial intelligence benefits from training on large datasets, trying to implement AI within the U.S. manufacturing industry poses a critical problem, according to Ismini Lourentzou, assistant professor in the Department of Computer Science and faculty at the Sanghani Center for Artificial Intelligence and Data Analytics. “Manufacturers not only tend to be slow and repetitive with data collection efforts, but they typically keep their data secret and partnerships are rare,” she said.

Lourentzou was recently awarded an EArly-concept Grant for Exploratory Research (EAGER) from the National Science Foundation for a project, Cost-sensitive Federated AI for Smart Manufacturing Data-Sharing, to develop a manufacturing service infrastructure that would encourage U.S. manufacturers to accelerate the use of AI in smart manufacturing and exchange data with trusted partners.  

Ran Jin, associate professor in the Grado Department of Industrial and Systems Engineering is serving as co-principal investigator for the project.

The proposed cost-sensitive data-sharing framework can assess and differentiate the contributions from multiple manufacturing data owners via a learned hierarchical task-driven similarity that decomposes the underlying retrieval scoring mechanism into two interconnected elements, manufacturer similarity and data similarity. 

“It can be used by manufacturers who wish to improve AI model training, testing, and deployment within their organizations or find potential collaborators and partners for new product development,” Lourentzou said. “Long-term, we hope that establishing a data-sharing market will enhance the United States’ international market share.”


Sanghani Center Student Spotlight: Mohannad Elhamod

Graphic is from the paper “Hierarchy-guided Neural Networks for Species Classification”

As he was looking for Ph.D. programs in computer science, Mohannad Elhamod happened upon the Science-Guided Machine Learning lab headed by Anuj Karpatne, an assistant professor and faculty at the Sanghani Center. “I was very excited about the work he was doing and after attending a Graduate Preview Weekend where I was delighted by the diversity of academic and social activities in the Department of Computer Science, I was pretty much convinced that Virginia Tech was where I should be.”

Now a student in that lab where science and machine learning meet, and with Karpatne as his advisor, Elhamod works on highly interdisciplinary research with experts from many domains, including machine learning, biology, and physics. 

“Being part of the Sanghani Center has also helped me connect with many experienced professors and passionate students,” he said.

In his research, Elhamod, who said that “the beauty and neatness of science” has been a fascination of his since he was a kid, is specifically interested in approaching science-guided machine learning through the lens of visualization and interpretability that can foster safer and more responsible use of machine learning.  

“In recent years, machine learning has pervaded most aspects of our lives and led to breakthroughs in many domains. Businesses and researchers often rush to adopt machine learning into their workflows, with little understanding of how it works or its potential pitfalls like generating racist content, recommending unsafe medical treatments, and creating cyber-security loopholes,” Elhamod said. “My interest lies in promoting ‘explainable machine learning,’ a relatively recent research direction that advocates for machine learning models that are more transparent and interpretable by humans.” 

Elhamod’s collaborative work has been presented at conferences, workshops, and symposiums and published in peer-reviewed journals and conference publications. These include “Hierarchy-guided neural network for species classification” in Methods in Ecology and Evolution (2021), which he presented at the Association for the Advancement of Artificial Intelligence (AAAI) Second Symposium on Science-Guided AI (SGAI); Biodiversity Image Quality Metadata Augments Convolutional Neural Network Classification of Fish Species in Metadata and Semantic Research (2021), which won the Best Research Paper Award at the Research Conference on Metadata and Semantics Research; “Learning Physics-guided Neural Networks with Competing Physics Loss: A Summary of Results in Solving Eigenvalue Problems” at the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences and published by Oxford University Press; and “Biology-guided neural network for fish trait discovery,” published in Integrative and Comparative Biology 2021 and nominated for the Best Student Paper Wake Award by the Society for Integrative and Comparative Biology.

Elhamod earned a master’s of engineering degree from McGill University and a bachelor’s degree in computer engineering from Jordan University of Science and Technology. He is projected to receive his Ph.D. in Spring 2023.

“My passion has always been to teach, to be a professor where I can facilitate learning and feed the scientific passion of my students. However, over the past two years I have also become increasingly aware of and interested in many amazing research opportunities at private and national research labs,” he said. “So, I am hoping I will somehow manage to fulfill both interests after graduation.”


Sanghani Center Student Spotlight: Jie Bu

Graphic is from the paper “Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)” 

Jie Bu, a Ph.D. student in computer science, has been interested in machine learning since he was an undergraduate in communications engineering at Harbin Institute of Technology, China. There he was introduced to the Random Forests (a machine learning model) and genetic algorithms which, Bu said, still hold great fascination for him.

In his current research at the Sanghani Center, Bu uses machine learning for physical applications. 

“We have been exploring how we can use machine learning to help fluid dynamics and quantum physics. From knowledge of science developed over the centuries, we are seeking to find out how machine learning models can be made more interpretable and generalizable,” said Bu.

“Sometimes generating simulation data is very slow so we are looking at the possibility of using machine learning to accelerate the simulation,” he said. “Machine learning  is very powerful and can be used to greatly benefit science discovery.” 

Bu is also interested in improving the efficiency of deep learning in a number of ways, including better model architecture and network pruning. 

A paper with his advisor, Anuj Karpatne focuses on model architecture. Bu presented their work on “Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEs” last Spring in proceedings of the 2021 SIAM International Conference on Data Mining (SDM). They developed a new class of quadratic residual networks offering better accuracy, parameter efficiency, and convergence speed for solving forward and inverse problems in physics involving partial differential equations (PDEs).

At the Sanghani Center, Bu said, he has been able to team up and “meet with a lot of brilliant minds.” At the 2021 Neural Information Processing Systems (NeurlPS) conference in December, he presented “Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM),” collaborative work with his advisor and other Ph.D. students at the Sanghani Center that uses network pruning.

Bu was also on the research team for the paper, “PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly,” published both in proceedings at SDM 2020 and in Big Data Journal. 

Projected to graduate in summer 2023, Bu would like to continue his research in an industry aligned with his research direction.


Congratulations to Sanghani Center 2021 Summer and Fall Graduates

Virginia Tech’s Fall Commencement ceremony for the Graduate School is now underway (livestream here) and seven students from the Sanghani Center are among those receiving degrees. 

“This has been a tough year and they successfully navigated obstacles caused by the COVID19 pandemic to achieve their academic goals and we are very proud of them,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science at Virginia Tech and director of the Sanghani Center for Artificial Intelligence and Data Analytics

Following is a list of Sanghani Center 2021 summer and fall graduates:

Ph.D.

Khoa Doan, advised by Chandan Reddy, has earned a Ph.D. in computer science.  His primary research interests lie in Machine Learning and Information Retrieval. The title of his dissertation is “Generative models meet similarity search: efficient, heuristic-free and robust retrieval”.  Doan has joined Baidu Research as a machine learning researcher. 

You Lou, co-advised by Bert Huang and Naren Ramakrishnan, earned a Ph.D. in computer science. His research areas are structured prediction, probabilistic graphical models, variational inference, and deep generative models. The title of his dissertation is “Modeling Structured Data with Invertible Generative Models.” Lou has joined Motional, a driverless technology company, as a machine learning research scientist.

Anika Tabassum, advised by B. Aditya Prakash, has earned a Ph.D. in computer science. She also earned the Urban Computing graduate certificate. For her Ph.D. research, she worked to develop explainable and domain-guided machine learning frameworks for power systems to aid decision-making for emergency management authorities. The title of her dissertation is “Explainable and Network-based Approaches for Decision-making in Emergency Management.” Tabassum has joined Oak Ridge National Laboratory in Tennessee as a postdoctoral research associate in the Discrete Algorithms Group, working on various projects related to scientific machine learning. 

Tian Shi, advised by Chandan Reddy, has earned a Ph.D. in computer science. His primary research interests lie natural language processing and machine learning. The title of his dissertation is “Novel Algorithms for Understanding Online Reviews.” Shi has joined Moody’s Analytics as a machine learning research scientist.

Ping Wang, advised by Chandan Reddy, has earned a Ph.D. in computer science. Her primary research focuses on question answering, graph mining, information extraction, and survival analysis with their applications in the healthcare domain. The title of her dissertation is “Automatic Question Answering and Knowledge Discovery from Electronic Health Records.” Wang has joined the Computer Science Department at Stevens Institute of Technology in Hoboken, New Jersey, where she is an assistant professor.  

Master’s Degree

Eman Abdelrahman, advised by Edward Fox, has earned a master’s degree in computer science. Her research interest lies in applying machine learning and natural language processing on Arabic scientific datasets such as ETDs in order to improve the accessibility to Arabic scientific data. The title of her thesis is “Improving the Accessibility of Arabic ETDs with Metadata and Classification.” She is remaining at Virginia Tech and the Sanghani Center to pursue a Ph.D. in computer science, advised by Ismini Lourentzou. 

Aarathi Raghuraman, advised by Lenwood Heath, has earned a master’s degree in computer science. Her primary research interests lie in biomedical data science and bioinformatics. The title of her thesis is “Predicting Mutational Pathways of Influenza A H1N1 Virus using Q-learning. Raghuraman has joined LexisNexis Legal and Professional in Raleigh, North Carolina, as a data scientist.

Esther Robb, advised by Jia-Bin Huang, has earned a master’s degree in computer engineering. Her primary research interests lie in reinforcement learning and data-efficient learning. The title of her thesis is “Data-Efficient Learning in Image Synthesis and Instance Segmentation.” Robb is pursuing a Ph.D. in computer science at Stanford University.


Sanghani Center Student Spotlight: Shailik Sarkar

Graphic is from the paper “Deep diffusion-based forecasting of COVID-19 via incorporating network-level mobility information”



Growing up in a family that included a doctor and public sector employees, Ph.D. student Shailik Sarkar said it became increasingly evident to him that social, behavioral, and economic factors often influence the physical and mental health patterns of an individual or a group of people.

That realization shaped his own decision to focus his research in computer science on exploring how data mining and artificial intelligence can be used to tackle community healthcare problems. 

A community level health outcome generally indicates overall health status of a group of people in a region, said Sarkar. “Anything from the cumulative number of people infected by COVID-19 to the number of people with asthma or the total number of deaths due to mental health conditions can be regarded as community level outcome of a certain physical or mental health issue. Analyzing how socioeconomic, linguistic, mobility, or any other features can be used to predict or identify those areas is something that is interesting to me,” he said.

Sarkar began his graduate studies at Virginia Tech as a master’s degree student having graduated with a bachelor of technology degree in computer science and engineering from Jalpaiguri Govt Engineering College, India (at the time affiliated with West Bengal University of Technology WBUT). But in Spring 2021, he converted to the Ph.D. program. 

His advisor is Chang-Tien Lu and the opportunity to work with him in his Spatial Data Mining Lab is one of the things that attracted him to the university, Sarkar said.

“As a student at the Sanghani Center I have the chance to work with people from different backgrounds, each bringing their own unique perspective,” Sarkar said. “I like the center’s continuous drive towards tackling new problems and the singular focus towards exploring new research areas in artificial intelligence.”

Sarkar was part of the research team on the paper: “Deep diffusion-based forecasting of COVID-19 via incorporating network-level mobility information,” recently included in the proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). This paper was a collaboration between researchers at the Hume Center and the Sanghani Center.

Sarkar is also pursuing the National Science Foundation-sponsored Urban Computing certificate.

“What I learned from UrbComp has helped me massively in understanding how ubiquitous sources of data can be used to tackle the kinds of problems I am working on,” said Sarkar. “The program introduced to me topics like epidemiology, event detection, and several other challenge areas that AI and computer science in general can be used for.”

After earning his Ph.D., Sarkar would like to hold a position in either academia or industry where he can apply insights from his research in AI to real world solutions in healthcare.


Virginia Tech researchers garner NSF grant to connect AI with urban planning to improve decision making and service delivery

Tom Sanchez (left) and Chris North (right)

Tom Sanchez, professor of urban affairs and planning, and Chris North, professor of computer science and associate director of the Sanghani Center for Artificial Intelligence and Data Analytics, have been awarded a planning grant from the National Science Foundation’s Smart and Connected Communities program. Click here to read about how they will combine their expertise to use cities’ data collection and algorithm deployment to develop creative solutions to urban planning processes that have previously relied on traditional, analog approaches.


Sanghani Center Student Spotlight: Yi Zeng

Graphic is from the paper “‘Rethinking the Backdoor Attacks’ Triggers: A Frequency Perspective’”

At the International Conference on Computer Vision (ICCV 2021) earlier this month, Yi Zeng, a Ph.D. student in electrical and computer engineering, gave a poster presentation on “Rethinking the Backdoor Attacks’ Triggers: A Frequency Perspective.”

Among the paper’s collaborators is his advisor Ruoxi Jia. Zeng was a master’s degree student at the University of California San Diego when he became aware of Ruoxi (at the University of California Berkeley at the time) and her achievements in trustworthy machine learning. 

“When I started to look at Ph.D. programs, an internet search led me to Dr. Ruoxi’s website. By then she was at Virginia Tech and I decided to contact her,” said Zeng. “After several interactions, I knew I would like to work with her. Her unique understanding of deep learning robustness and theoretical background, along with her warm nature, are the very things that drew me to Virginia Tech and the Sanghani Center.”

Zeng said that from the beginning he has “felt the support from my department and the Sanghani Center for marketing our work on various platforms and the culture of collaboration to achieve remarkable results. I like how the Sanghani Center brings together experts in artificial intelligence and we aim for higher things to accomplish.”

Zeng’s research addresses some recent studies showing that deep learning models can be misled and evaded in ways that would have profound security implications under adversarial attacks. He is aiming to safeguard deep learning from theory to algorithm to practice and has developed several practical countermeasures that achieved state-of-the-art effectiveness with theoretical analyses. 

The paper he presented at ICCV investigates backdoor attacks on deep learning models. 

“Such training time attacks cause models to misbehave when exposed to inputs with specified triggers while maintaining top-tier performance on clean data,” Zeng said. “We developed a new concept and technique for unlearning potential backdoors in a model that has been backdoored. Because backdoor attacks have already demonstrated their ability to impair face recognition, autonomous driving, and authentication systems, this work can be adopted as one of the most general and optimal methods for removing and mitigating such potential security vulnerabilities in deep learning models.”

Before beginning his Ph.D. program at Virginia Tech, Zeng focused more on empirical or application efforts on deep-learning-related security issues. One intriguing aspect of empirical works revealed in recent years, he said, is that they can be easily circumvented with carefully designed adaptive attacks if the attackers know the protective pattern. 

“As a result, I believe that the future of artificial intelligence security will necessitate substantial theoretical support and only by addressing the fundamental security challenges inherent in data science will we be able to unleash the full potential of data science,” he said.

Zeng presented a number of conference papers while earning his master’s degree including “Defending Adversarial Examples in Computer Vision based on Data Augmentation Techniques,” which garnered the Best Paper Award at the International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP) in 2020.

Projected to graduate in 2026, Zeng would like to continue his research on the general robustness of deep learning with a more academic focus.


Researchers receive grant to predict the mechanics of living cells

(From left) Anuj Karpatne, Department of Computer Science and Sanghani Center for Artificial Intelligence and Data Analytics; Amrinder Nain and Sohan Kale, both in the Department of Mechanical Engineering, meet in the STEP Lab. Photo by Peter Means for Virginia Tech.

With advances in deep learning, machines are now able to “predict” a variety of aspects about life, including the way people interact on online platforms or the way they behave in physical environments. This is especially true in computer vision applications where there is a growing body of work on predicting the future behavior of moving objects such as vehicles and pedestrians. 

“However, while machine-learning methods are now able to match — and sometimes even beat — human experts in mainstream vision applications, there are still some gaps in the ability of machine-learning methods to predict the motion of ‘shape-shifting’ objects that are constantly adapting their appearance in relation to their environment,” said Anuj Karpatne, assistant professor of computer science and faculty at the Sanghani Center for Artificial Intelligence and Data Analytics. Click here to read how Karpatne and his team will tackle this challenge in their National Science Foundation-sponsored research.


Sanghani Center Student Spotlight: M. Maruf

Graphic is from the paper “Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling Approach”


Having the opportunity to apply state-of-the-art machine learning models to bioinformatics problems as an undergraduate motivated M. Maruf to take a deep dive into machine learning and deep learning as a Ph.D. student in computer science at Virginia Tech which he chose because of its exemplary research and top-notch facilities. 

“Dr. Anuj Karpatne’s unique view towards solving real-world problems fascinated me to explore more knowledge-infused machine learning,” Maruf said of his advisor at the Sanghani Center.

Last April, Maruf presented their collaborative paper, “Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling Approach,” at the SIAM International Conference on Data Mining (SDM).

Maruf’s research interests lie in the broad domains of science-guided machine learning and its applications with a focus on integrating domain knowledge into machine learning models to obtain generalized solutions consistent with scientific knowledge. 

“In particular, I am developing new algorithms for graph neural networks that allow for better representation with coherent knowledge propagation,” he said.

A  black-box neural network model learns solely from training samples and requires a lot of annotated real-world observations to learn the underlying patterns accurately, said Maruf. Moreover, black-box Artificial Neural Networks (ANN) ignore external biological knowledge in the training phase, resulting in inconsistent outputs.

“I am currently addressing these challenges for the fish trait segmentation problem by incorporating biological knowledge into the state-of-the-art ANN model,” he said.

Maruf presented “Biology-Guided Neural Network for Fish Trait Discovery,” at the Society for Integrative and Comparative Biology Virtual Annual Meeting earlier this year.

The Sanghani Center environment, Maruf said, provides its students with multidisciplinary learning and research collaboration opportunites.

His additional work with faculty and other Ph.D. students at 2021 conferences includes

“PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics”in proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), held in August; and “Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM),” which will be included in proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS) in December.

Maruf received a bachelor’s degree in computer science and engineering from Bangladesh University of Engineering and Technology.  

His projected graduation date is Spring 2021 and he plans to pursue an industrial research position.


Sanghani Center Student Spotlight: Si Chen

Graphic is from the paper “Knowledge-Enriched Distributional Model Inversion Attacks”

With privacy a growing concern, Si Chen, a Ph.D. student in the Bradley Department of Electrical and Computer Engineering is using machine learning to study potential attacks and defenses against machine learning models. 

She was attracted to this area of research because it is important and practical in real-world settings.

“For example,” said Chen, “if a company trains a medical diagnosis model on a training set containing sensitive information, an attacker may be able to infer the training set’s knowledge even if he or she only has access to the model. Our job is to research better attack algorithms that can aid development of defense techniques.”

Chen is advised by Ruoxi Jia, faculty at the Sanghani Center. “I really enjoy the academic atmosphere, diverse and inclusive environment, and the college culture at Virginia Tech and at the center. My advisor and lab mates are wonderful people who are always willing to lend a helping hand.”  

In October, Chen will present the paper, Knowledge-Enriched Distributional Model Inversion Attacks at ICCV 2021. During the summer another paper that she and Ruoxi collaborated on — Zero-Round Active Learning — was published as an arvix preprint. Their previous paper, One-Round Active Learning, published on that site in Spring 2021.

Chen earned a bachelor’s degree in electrical and electronics engineering from the Beijing Institute of Technology.

Projected to graduate in 2024, Chen hopes to have an industry job where she can continue to work on her research area of interest.



Sanghani Center Student Spotlight: Muntasir Wahed

Graphic is from the paper “SAUCE: Truncated Sparse Document Signature Bit-Vectors for Fast Web-Scale Corpus Expansion”

Working toward a Ph.D. in computer science, Muntasir Wahed is delving into self-supervised learning, adversarial training, and out-of-distribution detection.

“Suppose we train a machine learning classifier to help medical diagnosis of a disease X given an X-ray,” Wahed said. “We collect a large dataset of X-rays for both positive and negative samples of the disease X. However, after we deploy the classifier in real life, it encounters confusing X-rays that have features not seen in any of the X-rays in the training samples. In such cases, it would be unreliable to classify the samples as positives or negatives. Instead, we would like to have a mechanism to recognize that these samples are so far unseen, or in other words, out-of-distribution.”

Recent self-supervised learning methods include contrastive training, which aims to bring closer pairs of positive examples (similar instances) and repel negative pairs (dissimilar instances). “But most instance-wise and cluster-based, or prototypical, contrastive learning techniques lack robustness against adversarial examples. That is what I am aiming to improve,” said Wahed.

Though he had been working on machine learning for the last three years, both in research and industrial settings, a Data Challenges in Machine Learning Course — taught by his advisor Ismini Lourentzou last Spring — really piqued his interest in self-supervised learning, adversarial training, and out-of-distribution detection. 

“The underlying challenges and the real-life implications of these problems intrigued me and after some background study, I recognized some areas to improve and started working on what is now his main research focus,” Wahed said.

In early November, Wahed will present “SAUCE: Truncated Sparse Document Signature Bit-Vectors for Fast Web-Scale Corpus Expansion” at the 30th ACM International Conference on Information and Knowledge Management (CIKM).

He is collaborating with Nur Ahmed, postdoctoral associate at MIT Sloan & MIT CSAI on “The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research.” This work has been featured at VentureBeatScientific AmericanAxiosMarginal Revolution; and in two AI reports, The National Security Commission on Artificial Intelligence and Stanford AI Index.

Wahed earned a bachelor’s degree in computer science from the University of Dhaka, Bangladesh. He was drawn to Virginia Tech and the Sanghani Center because of the diversity of the student body and the potential for research collaboration. As the Department of Computer Science and the Sanghani Center continue to grow, it opens even more doors to multidisciplinary research and learning opportunities, he said.

Projected to graduate in Fall 2024, Wahed hopes to find a position in a research laboratory where he can continue to work in collaborative settings on problems with real-life implications.


Sanghani Center Student Spotlight: Arka Daw

Graphic is from the paper “Physics-guided architecture (PGA) of neural networks for quantifying uncertainty in lake temperature modeling” 

Conferences have been a big part of Arka Daw’s life as a Ph.D. student this past academic year.

Daw presented “Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling” in proceedings at the 2020 SIAM International Conference on Data Mining (SDM), and “PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics” in proceedings at the 2021 ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).

In addition, he participated in the NeurIPS ML4PS Workshop, “Physics-Informed Discriminator (PID) for Conditional Generative Adversarial Nets.”

Daw’s broader research interests include artificial intelligence and deep learning but, more specifically, his work is geared towards formulating generic ways of coupling scientific knowledge with conventional deep learning approaches. 

The research he presented at the SIAM conference involved predicting the temperature of lakes at different depths. 

“Our proposed solution included a specialized design of a seq-to-seq model where a specific physics-driven inductive bias was infused directly into the model architecture,” said Daw, who is advised by Anuj Karpatne, “It demonstrated that combining scientific knowledge with deep learning models can not only improve their generalizability but also provide meaningful uncertainty estimates.”

During his undergraduate studies in electronics and telecommunication engineering at Jadavpur University, India, Daw had the opportunity to work on retinal artery-vein classification during a research internship at the Pattern Recognition and Image Analysis group at University of Muenster, Germany. 

“This is when I realized the immense potential of deep learning in solving real-world problems and decided to pursue higher studies in the broader field of artificial intelligence,” he said. 

Daw said he was drawn to Virginia Tech due to its eminence in world-class research and exemplary work of faculty in the Department of Computer Science and at the Sanghani Center.

“I am exceedingly fascinated by Dr. Karpatne’s approach to solving real-world scientific problems and how he works towards shaping the emerging field of science-guided machine learning,” he said. “I am very fortunate to have him as my advisor.”

Daw said that being a student at the Sanghani Center has provided him the opportunity to work with students and faculty of diverse backgrounds and research focus.  

“Everyone is always very supportive, really fun to work with, and provides great advice when you need it,” he said.

Daw is projected to graduate in Spring 2023.


Sanghani Center students spend summer months gaining real-world experience at companies, labs, and organizations across the country


Yue Feng, a Ph.D. student in electrical and computer engineering, is an intern with the Snap Research Creative Vision Team in Santa Monica, California.

With restrictions to working in physical office space still in effect, graduate students at the Sanghani Center are working remotely this summer for companies, labs, and programs from coast to coast. Students are not only gaining real-world experience from internships and other opportunities but, in many cases, they are also able to advance their own research interests.

Following is a list of Sanghani Center students and the work they are doing:

Badour AlBahar, a Ph.D. student in electrical and computer engineering, is a computer vision intern at Adobe Vision group in San Jose, California. She is working on human reposing and animation. Her advisor is Jia-Bin Huang.

Sikiru Adewale, a Ph.D. student in computer science, is a software development engineer intern at Amazon Web Service in Seattle, Washington. He is working on data transfer and storage on the AWS snowball device. His advisor is Ismini Lourentzou.

Vasanth Reddy Baddam, a Ph.D. student in computer science, is an research intern at Siemens in Princeton, New Jersey. He is working on contributing to industrial research projects on leveraging machine learning to analyze multi-agent reinforcement learning (MARL) algorithms and implement them. His advisor is Hoda Eldardiry. 


Subhodip Biswas
, a Ph.D. student in computer science, is working on Bayesian optimization techniques for automated machine learning (AutoML) and robust artificial intelligence systems as part of the Journeyman Fellowship he received from the DEVCOM Army Research Laboratory (ARL) Research Associateship Program (RAP) administered by the Oak Ridge Associated Universities (ORAU). His advisor is Naren Ramakrishnan.

Jie Bu, a Ph.D. student in computer science, is a research intern at Carbon 3D in Redwood City, California. He is working on artificial intelligence-powered computational geometry. His advisor is Anuj Karpatne.

Si Chen, a Ph.D. student in computer engineering, is a research intern at InnoPeak Technology in Seattle, Washington. She is working on research on model privacy protection. Her advisor is Ruoxi Jia.

Kai-Hsiang Cheng, a master’s degree student in computer science, is an intern at GTV Media Group in New York City. He is working on the content management system of the media’s platform. His advisor is Chang-Tien Lu.

Riya Dani, a master’s degree student in computer science, is a software engineer intern at Microsoft. She is working on web application developments under Azure. Her advisor is Ismini Lourentzou.

Debanjan Datta, a Ph.D. student in computer science, is an intern on the Amazon Web Services team at Amazon in Seattle, Washington. He is working on time series characterization and classification.  His advisor is Naren Ramakrishnan.

Arka Dawa Ph.D. student in computer science, is an applied scientist intern at Amazon Web Services Lambda Science Team in Seattle, Washington.  He is working on developing an automated causal machine learning framework for setting up experiments and estimating causal effects from observational data. His advisor is Anuj Karpatne.

Yue Feng, a Ph.D. student in electrical and computer engineering, is an intern with the Snap Research Creative Vision Team in Santa Monica, California. She is working on a 3D computer vision project. Her advisor is Jia-Bin Huang.

Chen Gao, a Ph.D. student in electrical and computer engineering, is a research intern at Google in Cambridge, Massachusetts. He is working on creating video panoramas using a cellphone. His advisor is Jia-Bin Huang.

Jianfeng He, a Ph.D. student in computer science, is an intern at Tencent AI Lab in Seattle,Washington. He is working on research about multi-modal dialogue with mentors Linfeng Song and Kun Xu. His advisor is Chang Tien-Lu.

Taoran Ji, a Ph.D. student in computer science, is an intern at Moody’s Analytics in New York City. He is working on analyzing credit and financial data for the global financial markets, which will drive algorithmic improvements in Moody’s Analytics core machine learning and artificial intelligence-driven products. His advisor is Chang-Tien Lu.

Adheesh Juvekar, a Ph.D. student in computer science, is a machine learning and natural language processing intern at Deloitte & Touche LLP. He is working on automatically extracting relevant information from transactional invoices using state of the art deep learning techniques. His advisor is Edward Fox.

M. Maruf, a Ph.D. student in computer science, is a machine learning engineering intern at Qualcomm GNSS/location team in Santa Clara, California. He is applying machine learning techniques to hybrid technology fusion for navigation/positioning in mobile, wearable, automotive, and micro-mobility applications. His advisor is Anuj Karpatne.

Nikhil Muralidhar, a Ph.D. student in computer science, received an Applied Machine Learning Summer Research Fellowship at Los Alamos National Lab in Los Alamos, New Mexico, to work with researchers on physics-informed machine learning for modeling adsorption equilibria in fluid mixtures. His advisor is Naren Ramakrishnan. 

Makanjuola Ogunleye, a Ph.D. student in computer science, is an application support engineer intern at Northwestern Mutual in Milwaukee, Wisconsin. His duties include coding, testing, and implementing complex programs from user specifications. He is also performing client data analysis to support engineering technology to improve and facilitate customer success. His advisor is Ismini Lourentzou.

Nishan Pokharel, a master’s degree student in computer science, is a software engineering intern at Capital One in Mclean, Virginia.  He is working on network infrastructure automation. His advisor is Chris North

Avi Seth, a master’s degree student in computer science, is serving as a graduate team leader this summer for Virginia Tech’s Data Science for the Public Good program. The group works on projects that address state, federal, and local government challenges around today’s relevant and critical social issues. His advisor is Ismini Lourentzou.

Mia Taylor, a master’s degree student in computer science, is a software development intern at Amazon Web Services in Seattle, Washington. Her team is working with Comprehend AutoML which allows customers to build customized natural language processing models using their own data. Her advisor is Lifu Huang.

Yiran Xu, a Ph.D. student in electrical and computer engineering, is an intern with the Snap Research Creative Vision Team in Santa Monica, California. He is working on 3D human reconstruction and video generation/manipulation. His advisor is Jia-Bin Huang.

Shuaicheng Zhang, a Ph.D. student in computer science, is a natural language processing (NLP) research intern at Deloitte in New York City. He is part of the Audit and Assurance AI innovation team, working on open information extraction on internal control files to help auditors effortlessly process these files. His advisor is Lifu Huang.

Yuliang Zou, a Ph.D. student in electrical and computer engineering, is a research intern at Waymo in Mountainview, California. He is working on the perception problem for self-driving cars.  His advisor is Jia-Bin Huang.


Congratulations to Sanghani Center Spring 2021 Graduates

Virginia Tech’s virtual university commencement will livestream tonight, Friday, May 14, at 6:15 p.m., and degrees will be conferred at this time.

“We are extremely proud of our graduates who achieved their goals despite more than a year of a pandemic that upended much of their lives,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science at Virginia Tech and director of the Sanghani Center for Artificial Intelligence and Data Analytics. “When everything went virtual, they continued to attend classes, meet with their advisors, conduct research, present papers at conferences, and work at internships — all testament to their perseverance and a good barometer of their future success .”

Following is a list of Sanghani Center graduates:

Ph.D.

Prashant Chandrasekar, advised by  Edward Fox, is receiving a Ph.D. in computer science. His research interest lies in digital libraries. The title of his dissertation is “Continuously Extensible Information Systems: Extending the 5S Framework by Integrating UX and Workflows.” Chandrasekar will join the University of Mary Washington as an assistant professor in computer science.


Kaiqun Fu
, advised by Chang-Tien Lu, is receiving a Ph.D. in computer science. His research interests lie in spatial data mining, machine learning, and graph neural networks, with a focus on social media analysis in intelligent transportation systems and smart cities. The title of his dissertation is “Spatiotemporal Event Forecasting and Analysis with Ubiquitous Urban Sensors.” Fu will join South Dakota State University as assistant professor in August 2021.

Yen-Cheng Lu, advised by Chang-Tien Lu, is receiving a Ph.D. in computer science. His research interests lie in anomaly detection and probabilistic modeling. The title of his dissertation is “Relational Anomaly Detection: Techniques and Applications.” Lu will be continuing his career as a software engineer at Amazon Alexa AI.

Sneha Mehta, advised by Naren Ramakrishnan, is receiving a Ph.D. in computer science. Her research interests are data mining and deep learning, especially for natural language processing applications. The title of  her dissertation is “New Methods for Event Detection and Extraction from News Articles.” Mehta will join Twitter as a machine learning researcher in July.

Sathappan Muthiah, advised by Naren Ramakrishnan, is receiving a Ph.D. in computer science. His areas of interest include forecasting, machine learning, information retrieval, and topic detection and tracking (TDT). The title of his dissertation is “Design and Maintenance of Event Forecasting Systems”.  Muthiah has joined eBay as an applied researcher.

Reza Sepasdar, advised by Anuj Karpatne, is receiving a simultaneous master’s degree in computer science and Ph.D. in civil engineering. (His master’s co-advisor is Maryam Shakiba). His research interests lie in the intersection of AI and computational mechanics. Sepasdar’s master’s thesis is entitled “A Deep Learning Approach to Predict Full-Field Stress Distribution in Composite Materials.” He will defend his Ph.D. dissertation, “Micro-mechanical Behavior of Fiber-reinforced Composites using Finite Element Simulation and Deep Learning,” this summer.

Sirui Yao, advised by Bert Huang, is receiving a Ph.D. in computer science. Her research interests include machine learning, recommender systems, and fairness. The title of her dissertation is “Evaluating, Understanding, and Mitigating Unfairness in Recommender Systems.” Yao will join Google in June 2021 as a machine learning engineer.

Master’s degree

John Aromando, advised by Edward Fox, is receiving a (coursework only) master’s degree in computer science. His research interests include natural language processing and information retrieval, particularly analyzing design specs written in natural language and synthesizing a machine-understood language via relevant descriptions.

Mohi Beyki, advised by Edward Fox, is receiving a master’s degree in computer science. His research interests are in deep learning, health care, and software engineering. The title of his thesis is “Synthetic Electronic Medical Record Generation using Generative Adversarial Networks.”  Beyki will be joining Google as a software engineer this summer.

Yi-Chun Chang, advised by Chang-Tien Lu, is receiving a master’s degree in computer science. His research interest is in using social media analytics to detect threats. The title of his thesis is “RISECURE: Metro Incidents And Disruptions Detection Using Social Media And Graph Convolution.”  He will join Walmart Global Tech as a software engineer in July.

Po-Han Chen, advised by Chang-Tien Lu, is receiving a master’s degree in computer science. His research focuses on using data from social media to help solve real-world problems. The title of his dissertation is “Metro Security Incidents And Threat Detection Using Social Media.” He will be joining Bloomberg as a software engineer this summer.

Yi Huang, advised by Jia-Bin Huang, is receiving a masters of engineering degree. His research interests lie in computer vision and machine learning. The title of his master’s project is “Cross-Domain Context-aware 3D Hand Pose Estimation.” Huang will join Qualcomm as a computer vision research engineer.

Kulendra Kumar Kaushal, advised by  Naren Ramakrishnan, is receiving a master’s degree in computer science. His research interests lie in the field of natural language processing and information extraction. The title of his thesis is “Information Extraction of Technical Details From Scholarly Articles.” Kaushal will be joining Bloomberg as a software developer.

Prathamesh Kalyan Mandke, advised by Anuj Karpatne, is receiving a masters of engineering degree. His research interests lie in machine learning and computer vision. The title of his master’s project is “Fluorescent Image Reconstruction in Shape Controlled Cell Migration using Deep Learning.”  Mandke will join Qualcomm AI Research as a machine learning software engineer in July.

Ashkan Nazari, advised by Lenwood Heath, is receiving a master’s degree in computer science. His research interests lie in artificial intelligence, deep learning, and cloud-based intelligence systems analysis. Nazari has also worked toward a Ph.D. in mechanical engineering. He will join the Silicon Valley-based luxury electric vehicle start-up Lucid Motors as a senior data scientist, working on developing intelligent battery initiatives.

Ioannis Papakis, co-advised by Anuj Karpatne and Abhijit Sarkar, is receiving a master’s degree in computer science. His research interests lie in machine learning, computer vision, robotics, and signal processing. The title of his thesis is “A Graph Convolutional Neural Network Based Approach for Object Tracking Using Augmented Detections With Optical Flow.”  Papakis also won first place in the 2021 Paul E. Torgersen Graduate Student Research Excellence Awards MS poster presentation category. Starting in July, he will be employed by Bertrandt US, Inc., working at Audi in Santa Clara, California, as an advanced driver-assistance systems engineer.

Arya Shahdi, co-advised by Anuj Karpatne and Bahareh Nojabaei, is receiving a master’s degree in computer science. His research interests lie in forecasting and geospatial modeling and analysis. The title of his thesis is “Physics-guided Machine Learning Approaches for Applications in Geothermal Energy Prediction.” Shahdi is a supply chain data scientist at Lowe’s Companies, Inc. 

Aarohi Sumant, advised by Edward Fox, is receiving a master’s degree in computer science. Her research focuses on deep learning and machine learning application, specifically in natural language processing. The title of her thesis is “Improving Deposition Summarization using Enhanced Generation and Extraction of Entities and Keywords.” Sumant will join Amazon as a software development engineer in July.

Omer Zulfiqar, advised by Chang-Tien Lu, is receiving a master’s degree in computer science. His research interests lie in social media event detection and natural language processing. The title of his thesis is “Detecting Public Transit Service Disruptions Using Social Media Mining and Graph Convolution.” He will join Walmart Labs as a software engineer in June.


UrbComp program team receives Alumni Award for Outreach Excellence

In this 2019 photo Colin Flynn, Vicki Keegan, and Susan Hembach from Loudoun County Public Schools meet at the Sanghani Center for Artificial Intelligence and Data Analytics with Ph.D. students Andreea Sistrunk, Subhodip Biswas, and Fanglan Chen to discuss how Redistrict is helping to establish school attendance zones. 

A multidisciplinary faculty team has garnered the Virginia Tech 2021 Alumni Award for Outreach Excellence for developing and administering the Urban Computing (UrbComp) program that trains graduate students in the latest methods of analyzing massive datasets to study key issues facing urban populations while emphasizing ethical and societal issues for practicing responsible data science.

The award, announced today by the university, accompanied by a $2,000 monetary award, is funded through the university’s Alumni Association and managed and administered by the Commission on Outreach and International Affairs.

The 24-member UrbComp team is led by Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science at Virginia Tech and director of the Sanghani Center for Artificial Intelligence and Data AnalyticsWanawsha Shalaby serves as UrbComp program coordinator.

Team faculty members represent 11 different departments who are either teaching an UrbComp course, advising an UrbComp student, or engaging in UrbComp related projects. They are: co-PIs Layne T. Watson, CS; Leanna House, STAT; Mark Embree, MATH; David Bieri, UAP; and the late John Ryan, SOC; contributing faculty Saifur Rahman, ECE; Kimberly Ellis, ISE; Ryan Gerdes, ECE; James Hawdon, SOC; Robert Hildebrand, ISE; Mike Horning, COMM; Eric Jacques, ECE; Scotland Leman, STAT; Chang-Tien Lu, CS; Brian Mayer, CS; Klaus Moeltner, AAEC; Chris North, CS; Hesham Rakha, CEE; Chandan Reddy, CS; Tom Sanchez, UAP; Nathan Self, CS; and Pablo Tarazaga, ME.

A major component of the UrbComp program — initially funded in 2015 by a five-year grant from the National Science Foundation’s Research Traineeship program — is outreach to industry, government, and the nonprofit sector, fostering collaborations that are beneficial both to students who gain real world experience and partners who can use data to make better decisions for their communities. Following are examples of such collaborations:

  • Loudoun County Public Schools (LCPS): School rezoning decisions often cause emotional stress for families and communities for a variety of reasons. Parents worry about continuity of programs and activities at a new school, the toll it might take on their children’s friendships, and modes of transportation. School officials, administrators, and staff want to ensure that all students have equitable access to educational programs and facilities. UrbComp has partnered with LCPS to tackle the issue of redrawing school attendance zones, designing Redistrict, an online interactive platform that tries to reduce stress by getting parents and other stakeholders more involved in the process. Since working with UrbComp, LCPS has established three new school attendance zones including Goshen Post and Waxpool Elementary Schools, which opened in Fall 2018 and 2019 respectively, as well as Lightridge High School, which opened in Fall of 2020.
  • World Wildlife Fund (WWF): UrbComp partnered with the WWF on one of the organization’s important issues, illegal logging and trade, often the first links in a chain of events that cause forest degradation, using — for the first time — an automated data analytics system to help identify suspicious timber trade records that relate to possible illegal activity. The human-machine approach developed for this innovative system will help flag suspicious timber at the border in real time, improving both efficiency and effectiveness. UrbComp students presented a paper on their work for this project at the Annual Conference on Innovative Applications of Artificial Intelligence, a collocated program of the Association for the Advancement of Artificial Intelligence conference held in New York City in February, 2020.
  • Washington Metropolitan Area Transit Authority (WMATA): The UrbComp program has collaborated with WMATA on five different projects. Four of these projects were course projects for UrbComp students who helped WMATA predict its system’s on-time performance (OTP); developed a methodology for assessing delays in the bus system by applying big data structure and statistical analysis to the data constantly collected by WMATA buses; created a model to predict ridership impacts on mass transit with artificial intelligence; and leveraged Twitter data for early detection of metro service disruptions and proposed the Metro Disruption Detection Model, which captures semantic similarities between transit lines in the underlying social media space. The latter has grown into a fifth sponsored project to further develop a deployable open-source system that detects criminal acts in real-time.

UrbComp is open to all graduate students at Virginia Tech and provides multiple avenues of engaging and participating in the program. Students can participate through the Graduate Certificate in Urban Computing, approved in 2017, and the two courses developed for this certificate (Intro to Urban Computing and Ethics and Professionalism in Computer Science).

Students can also engage in the program by attending any of multiple events organized by UrbComp, including the bi-weekly UrbComp Seminar Series, faculty and student retreats, workshops, and various networking events.

To date, 216 graduate students across 18 departments at Virginia Tech have been impacted by the UrbComp program and 20 UrbComp graduates (19 Ph.D.’s and one master’s degree) have gone on to jobs within industry, government and academia. Alumni have continued to participate and engage with the program long after graduating from Virginia Tech, returning to participate in UrbComp events, specifically as guest speakers in the bi-weekly UrbComp Seminar Series where they share their experiences in the program and how UrbComp has played a part in their professional careers. 

For more information on UrbComp, contact the program coordinator, Wanawsha Shalaby


Sanghani Center Student Spotlight: Ping Wang

 Graphic is from the paper “Text-to-SQL Generation for Question Answering on Electronic Medical Records”

In 2016, Ping Wang followed her advisor, Chandan Reddy, from Wayne State University, where she received a master’s degree in computer science, to Virginia Tech and the Sanghani Center.

Her area of interest is healthcare systems, which are undergoing many changes in the era of big data.

“Advances in artificial intelligence and digitization in healthcare have enabled healthcare providers to effectively sift through tremendous amounts of medical information,” said Wang. “My first research project in this direction was about survival analysis and my advisor Dr. Reddy and other group members provided many useful suggestions and help at the initial stage. After further investigation, I found that there are still many unique challenges in the healthcare domain. I hope to leverage my expertise in data mining and machine learning to solve real-world challenges and advance healthcare applications.”

While earning her Ph.D., Wang has been located, at different time periods, in both Arlington and Blacksburg. She said she has enjoyed her experiences on both campuses, maintaining regular meetings with Reddy and other group members to discuss her research and its progress.

“The professional environment for learning and conducting research at the Sanghani Center has offered me great research and collaboration opportunities,” Wang said.

Her research is focused on developing machine learning methods that can efficiently utilize Electronic Health Records (EHRs). These records contain medical and treatment history of patients to facilitate physicians’ decision making in their clinical practice.

Wang is looking at three aspects: (1) Clinical Question Answering: How to seek answers from EHRs for clinical activity related questions posed in human language without the assistance of database and natural language processing (NLP) domain experts; (2) Survival Analysis: How to predict when a medical event will occur and estimate its probability based on prior medical history of patients; and (3) Knowledge Discovery: How to discover underlying relationships between different events and entities in structured tabular EHRs and apply NLP techniques to construct structured events and knowledge base from clinical notes.

One of the goals in clinical question answering is to develop machine learning methods that can automatically seek answers from relational tables of the EHR database for human-language questions, she said. Traditionally, doctors interact with EHR via searching and filtering functions available in rule-based systems that first turn predefined-rules (user interface) to SQL queries, which will be executed on the database to retrieve patient information.

“These systems are complicated, difficult to manage, and require special training,” Wang said. “To tackle this problem, we proposed building a Text-to-SQL Query Translation System that can automatically translate clinical activity related questions to SQL queries, so that the doctors only need to type their questions in a search box to get answers. I also created a MIMICSQL dataset for question answering on tabular EHR to simulate a more realistic setting.”

This work, “Text-to-SQL Generation for Question Answering on Electronic Medical Records” was published at The Web Conference 2020.

Most recently, Wang presented “Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks” virtually at The Web Conference 2021.

Among her other published work is “Tensor-based Temporal Multi-Task Survival Analysis,” which was in the IEEE Transactions on Knowledge and Data Engineering in 2020.

Wang plans to defend her dissertation this summer and will join the Department of Computer Science at Stevens Institute of Technology as a tenure-track assistant professor for the Fall 2021 semester.


Sanghani Center Student Spotlight: Nurendra Choudhary

Graphic is from the paper 
“Self-Supervised Hyperboloid Representations Logical Queries over Knowledge Graphs”

Nurendra Choudhary was an applied science intern with the Amazon Search Team in Palo Alto, California, last summer where he worked on representation learning of products by leveraging the heterogeneous relations between them.

At The Web Conference 2021 last week, Choudhary, a Ph.D. student in computer science at the Sanghani Center, presented “Self-Supervised Hyperboloid Representations Logical Queries over Knowledge Graphs,” his research with data scientists at Amazon and his advisor Chandan Reddy.

It was Reddy’s research on deep learning methods in information retrieval that drew Choudhary to Virginia Tech. “It aligned well with my previous work in social media analytics and I felt that the Sanghani Center would be a great place to develop my expertise in a broader area,” he said.

Choudhary said that he was right. “I have benefited from being able to discuss my own research with a very diverse set of students working on many different problems and getting multiple diverse perspectives and possible solutions to my problems,” he said.

Choudhary’s primary research interest is representation learning with a focus on natural language processing and E-commerce.

Representation learning forms the foundation of most deep learning architectures, he said, and given the potential of change that an improvement in this area could bring, he was extremely interested in contributing to it.

“We notice a lot of E-commerce platforms being spammed by fake reviews,” said Choudhary. “An important pattern in these reviews is a lack of product detail and relevance. With better product and review representations, we can identify the spammers and provide a better customer experience.”

Choudhary has a bachelor’s degree in computer science and master’s degree in computational linguistics, both from the International Institute of Information Technology, Hyderabad, India.

Projected to graduate in 2023, he would like to pursue a career in industry research.


Sanghani Center welcomes new faculty member Ruoxi Jia

Ruoxi Jia, assistant professor of electrical and computer engineering and Sanghani Center faculty member

Ruoxi Jia, who joined the Bradley Department of Electrical and Computer Engineering at Virginia Tech as assistant professor in 2020, is the newest faculty member at the Sanghani Center for Artificial Intelligence and Data Analytics.

Jia’s research interest broadly spans the areas of machine learning, security, privacy, and cyber-physical systems. Her recent work focuses on building algorithmic foundations for data markets and developing trustworthy machine learning solutions. Towards that end, she and her group work on a range of projects, including data valuation and quality management, data privacy, active data acquisition, adversarial machine learning, and explainable machine learning.  

Jia is teaching a course on “Trustworthy Machine Learning” this semester and is looking for postdocs and Ph.D., master’s, and undergraduate students to join her group. Because of the limitations of personal contact due to COVID-19, she is happy to work with them remotely. (Interested students should click here for more information.)

“We extend a warm welcome to Ruoxi,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science and director of the Sanghani Center. “Her work in privacy and security aspects of machine learning can complement a range of work happening at the center.”

“I am excited to join the Sanghani Center and look forward to collaborating with the other faculty members and students to push the frontiers of data science and unleash the power of data in a trustworthy, responsible way,” said Jia.

Jia earned a bachelor of science degree from Peking University in 2013 and a Ph.D. in electrical engineering and computer sciences from the University of California Berkeley in 2018. 

She is the recipient of several fellowships, including the Chiang Fellowship for Graduate Scholars in Manufacturing and Engineering, the 8108 Alumni Fellowship, and the Okamatsu Fellowship. In 2017, she was selected for Rising Stars in EECS.

Prior to joining Virginia Tech she served as a postdoc in the Computer Science Department at University of California, Berkeley.

Her work has been published at professional conferences and featured in multiple media outlets, including MIT Technology Review, IEEE Spectrum, and Synced.


Hoda Eldardiry receives Early Career Science Award from Purdue University

Hoda Eldardiry, associate professor in the Department of Computer Science and faculty member at the Sanghani Center

Hoda Eldardiry, associate professor in the Department of Computer Science and faculty at the Sanghani Center for Artificial Intelligence and Data Analytics, has received an Early Career Science Award from her alma mater, Purdue University. The award is granted to alumni who have graduated in the last 10 years or are under the age of 40 and who illustrate promise of becoming a leader among their peers.

She was honored during a Distinguished Science Awards virtual event hosted by the Purdue University College of Science on April 14.

Eldardiry, who earned a master’s (2006) and PhD (2012) in computer science, in addition to an MBA (2011), was recognized for “enormous contributions to artificial intelligence and the computing research community at large.” Her research on artificial intelligence has led to the development of AI and machine learning that tackle complicated modern issues such as fraudulent medical claims, cyber threat protection, and sensors that can assist with panic attacks.

She has published over 35 peer-reviewed papers and holds 19 patents. She has managed research projects for government and commercial sectors with her own share of the awards exceeding $12 million.

Her other honors include the Creative Young Engineer Award and the Xerox Innovation Group Research Recognition Award for Advancing the Edge of Innovation.

Determined to support tomorrow’s future scientists, Eldardiry has also served as a professional mentor for the U.S. State Department TechWomen Program, a Bureau of Educational and Cultural Affairs initiative to combine global business, technology, and education power.


Sanghani Center Student Spotlight: Xinyue Wang

Graphic is from the paper “The Case For Alternative Web Archival Formats To Expedite The Data-To-Insight Cycle”

Xinyue Wang was an undergraduate research assistant involved in artificial intelligence and digital library research at the University of North Texas when he had occasion to connect with Edward Fox, professor in Virginia Tech’s Department of Computer Science and faculty at the Sanghani Center and Zhiwu Xie, a professor at University Libraries.

The two are now Wang’s co-advisors as he pursues a Ph.D. in computer science at Virginia Tech. “They are wonderful people and I am grateful to be able to learn from them and work with them,” he said.

Wang’s research interest is digital infrastructure and analytics of the digital library field. His current work involves digital infrastructure design for easy access and analysis of large web archive collections.

“Large web archive collections are rich datasets that are under researched due to their large size and complexity and have become a technical wall for researchers with or without computer science background. Lack of infrastructure design also makes it difficult for smaller institutions to provide easy access on such data,” Wang said. “My research aims to find a solution whereby large web archive collections can be efficiently accessed and analyzed for academia.”

This research, he said, would contribute to building a foundation for many other researchers who are interested in exploring web archive data in various fields.

“At Virginia Tech and the Sanghani Center I have had the opportunity to work with researchers in different fields, trying to use my own computer science expertise to help solve their problems,” Wang said. “I enjoy being in touch with a diverse group of researchers and confronting different real-world problems.”

Wang’s paper, “The Case For Alternative Web Archival Formats To Expedite The Data-To-Insight Cycle,” was included in the proceedings of the 2020 ACM/IEEE Joint Conference on Digital Libraries (JCDL) in 2020.

In previous years, Wang had two posters in JCDL conference proceedings, “Web Archive Analysis Using Hive and SparkSQL” in 2019; and “Towards A Self-Learning Library For Vibration Data” in 2018.

His work on “Metadata records machine translation combining multi‐engine outputs with limited parallel data,” was published in the Journal of the Association for Information Science and Technology In January 2018.

Wang, who earned his bachelor of science degree from the University of North Texas, is projected to graduate in June 2022 and plans to pursue a career in academia.


Data scientists combat hate crimes and other violence

Research associates Brian Mayer (top) and Nathan Self (bottom) meet virtually to review targeted violence events on the dashboard developed by the Sanghani Center.

About the series: Every complex problem has many multidisciplinary angles. Leveraging expertise and energy, Virginia Tech faculty and students serve humanity by addressing the world’s most difficult problems.

With risk of political and targeted violence on the rise across the United States, national and local leaders are asking Princeton University’s nonpartisan Bridging Divides Initiative (BDI) to provide them with more timely, reliable, and context-specific data on targeted violence events that could help them engage locally and better inform their policy decisions. 

As part of their response to this plea, BDI’s team of Princeton social scientists collaborated with data scientists at the Sanghani Center for Artificial Intelligence and Data Analytics to identify targeted violence events. These often include hate crimes and other incidents that target individuals because of their race, religion, sexual orientation, or other perceived characteristics. Click here to read more about this research.


Sanghani Center welcomes new faculty member Ismini Lourentzou

Ismini Lourentzou, assistant professor of computer science and Sanghani Center faculty member

The Sanghani Center for Artificial Intelligence and Data Analytics welcomes new faculty member Ismini Lourentzou, who joined the Virginia Tech Department of Computer Science as assistant professor in the Spring 2021 semester.

Lourentzou most recently served as a research scientist at IBM Almaden Research Center where she worked on machine learning, natural language processing, and information retrieval problems. In 2019, she was recognized with an IBM Invention Achievement Award and was selected to participate in Rising Stars in EECS.

In 2014, she received a Microsoft Azure Research Award.

Lourentzou said that the work of the Sanghani Center was one of the plusses that drew her to Virginia Tech.

Her research centers around machine learning challenges related to data — for example, learning with limited imperfect supervision, multimodal representation learning, and sequential decision making. This spring, Lourentzou is teaching a related course on Data Challenges in Machine Learning. Her current projects involve active and semi-supervised learning, self-supervision, interpretability, and graph adversarial learning. 

“We are delighted to have Ismini on our team,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science and director of the Sanghani Center. “Her work in the areas of NLP, especially language modeling and human-in-the-loop learning, ties into many projects underway at the center. Our students will greatly benefit from her courses and expertise in this cutting-edge area.”

She received her Ph.D. in computer science from the University of Illinois at Urbana – Champaign.

Lourentzou earned two bachelor’s degrees, one in computer science from the Athens University of Economics and Business, Greece, and one in business administration from the University of West Attica, Greece (formerly the Technological Educational Institute of Athens). 


Edward Fox honored as inaugural inductee in ACM SIGIR Academy

Edward Fox, professor of computer science and Sanghani Center faculty member

The Association for Computing Machinery Special Interest Group on Information Retrieval (ACM SIGIR) has announced that Edward Fox, professor in the Virginia Tech Department of Computer Science and faculty at the Sanghani Center for Artificial Intelligence and Data Analytics, is one of 25 inductees from across the globe in its inaugural SIGIR Academy. 

The inaugural academy inductees, drawn from academia, industry, and beyond, are principal leaders in information retrieval whose significant contributions have shaped the discipline or industry. Click here to read more about Edward’s inauguration.


Sanghani Center Student Spotlight: Xiaolong Li

Graphic is from the paper “Category-Level Articulated Object Pose Estimation.”

Xiaolong Li is a Ph.D. student in computer engineering. His main interest is in computer vision, with a focus on deep 3D representations learning for dynamic scene understanding. 

“Building robust smart algorithms will help machines understand the 3D world around us,” Li said.

“As human beings, we use our hands to interact with different objects like tools, and complete physical tasks,” Li said. “But if we had a depth camera that could capture points on the visible surface of human hands and the grasped object, we could estimate the pose of both human hands and objects, that is, the 3D locations of the hand joints, together with the location and orientation of the object, robustness under occlusions and generalizability to novel objects or novel hands.”

Li, who is advised by A. Lynn Abbott, presented his collaborative paper Category-Level Articulated Object Pose Estimation at the virtual 2020 Conference on Computer Vision and Pattern Recognition (CVPR) last June.

The paper, addressing the task of category-level pose estimation for articulatedobjects from asingle depth image, presented a novel category-level approach that correctly accommodates object instances previously unseen during training. The study introduced Articulation-aware Normalized Coordinate Space Hierarchy (ANCSH) — a canonical representation for different articulated objects in a given category. By leveraging the canonicalized joints, the researchers were able to demonstrate improved performance in part pose and scale estimations using the induced kinematic constraints from joints; and a high accuracy for joint parameter estimation in camera space.

“The Sanghani Center has provided me with the opportunity to collaborate with excellent researchers from diverse backgrounds,” Li said.

Li earned a bachelor’s degree in electrical engineering from Huazhong University of Science and Technology in China.

Projected to graduate in Spring 2022, he would like to find a position where he can devote his work to augmented reality (AR) and virtual reality (VR) research for smart agents interacting with a 3D environment.


Sanghani Center Student Spotlight: Jesse Harden

Graphic is from the paper “A Specification Language for Matching Mistake Patterns with Feedback.”

Ph.D. student Jesse Harden’s current research is focused on large, high-resolution displays and their use in and benefits for data science.

“I am particularly interested in how we can better design software for large displays for data science. And in the future, I hope to look into how machine learning can be used to improve interactions with large screen UIs for both individual and collaborative use scenario,” said Harden, whose concentration in this area was influenced by reading the past works of his advisor, Chris North, and through their subsequent discussions.

Harden’s prior research is focused on minimizing instructor effort in automating feedback for computer science education.

“Given the time constraints that instructors have, making it easier and quicker to specify pattern-matching tests that give appropriate feedback can enable them to write more in-depth automated feedback tests which may, in turn, help student learning,” he said.

He developed a specification language — that currently works for Pedal, a feedback infrastructure for Python — to make it easier for busy instructors to create tests coupling mistake patterns with feedback.

Harden’s paper on this topic, “A Specification Language for Matching Mistake Patterns with Feedback,” was published last week in the proceedings of the 2021 SIGCSE 52nd ACM Technical Symposium on Computer Science Education.

Harden earned a bachelor’s degree in mathematics with a concentration in statistics, and a master’s degree in data and information management, both from Radford University.

Looking at Ph.D. programs in computer science, Harden said he was attracted to Virginia Tech and the Sanghani Center by several factors: “great people, great opportunities, and great location.”

Before making a final decision, Harden said he had quite a few great interactions with both current students and professors which helped him solidify his choice.

“Now as a Ph.D. student at the Sanghani Center, I like being able to work with very knowledgeable individuals on interesting challenges and the benefit of available funding opportunities,” he said.

Harden is projected to graduate in Spring 2024 and would like to pursue a career in academia as a professor.


Sanghani Center Student Spotlight: Brian Keith

Graphic is from the paper “Narrative Maps: An Algorithmic Approach to Represent and Extract Information Narratives”

Having earned two bachelor of science degrees, one in mathematics and one in engineering, and a master’s degree in informatics, all from UniversidadCatólica del Norte, Chile, Brian Keith was looking for a flexible Ph.D. program. The Virginia Tech Department of Computer Science provided that flexibility and led him to the Sanghani Center where interdisciplinary data science is a key focus.

In his Ph.D. research, Keith, co-advised by Chris North and Tanushree Mitra —  is exploring online information narratives, in particular, how to represent, extract, and visualize them. He is also analyzing the issue of how misinformation spreads in these narratives.

“As an example,” Keith said, “consider how the coronavirus narrative evolved in the news during its first month, starting from a few articles mentioning it as a mysterious virus in China, to reports of new cases in other countries, and then an explosion of news regarding its spread and lethality. Or more recently, consider the U.S. presidential election and how different narratives and counter-narratives have been spreading, interlaced with misinformation and fake news. Understanding how these narratives are created, how they spread, and how they influence people is key to properly countering misinformation campaigns.”

Last month, Keith presented “Evaluating the Inverted Pyramid Structure through Automatic 5W1H Extraction and Summarization”  at the 2021 Computer + Journalism Symposium. In this paper, the researchers analyzed news articles in an attempt to measure how well they adjust to the “inverted pyramid” structure used by journalists.

“To do this we extract the answers to the basic journalistic questions who, what, where, when, why, and how) and compute a mathematical score. One of the potential applications of this could be to detect fake news or evaluate credibility, under the assumption that fake articles likely do not follow professional journalism standards like using ‘proper’ structure,”  Keith said.

During a separate session at the symposium, Keith discussed his work on “Narrative Maps: An Algorithmic Approach to Represent and Extract Information Narratives.”  Last fall, he presented the full paper on this research at the 23rd ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2020).

Keith is currently on track to receive his Ph.D. in Spring 2023. When he graduates he plans to return to his alma mater, Universidad Católica del Norte, where he will have a position as assistant professor as part of the Fulbright Faculty Development Program.


Sanghani Center Student Spotlight: Yali Bian

Yali Bian, Ph.D. student in the Department of Computer Science
Graphic from the paper “DeepVA: Bridging Cognition and Computation through Semantic Interaction and Deep Learning” 

According to Yali Bian, the Sanghani Center’s proclivity for encouraging interdisciplinary research is an added benefit while working on his dissertation topic, “Interactive Deep Learning for Semantic Interaction.” It encompasses several different research areas like human computer interaction, deep learning, visual analytics, and explainable AI. 

Bian is exploring ways to provide user-friendly interactive visualization systems to users unfamiliar with deep learning so that they can make full usage of powerful deep learning models.

A good example, he said, is building a truly hybrid human-AI co-learning system than can assist intelligence analysts in their sensemaking tasks. The analysts could gain useful feedback from powerful deep learning models without knowing how to manipulate the model.  

“My interest in this particular topic stems from wanting to address what I think of as a ‘last mile problem,’’’ said Bain, who is advised by Chris North. “I would like to see powerful machine learning models fully accessible to laypeople – to the extent that they could design a personal model.”

In April, his paper, “DeepSI: Interactive Deep Learning for Semantic Interaction,” will be included in proceedings at the ACM IUI2022 conference. The study proposes framework that can be fine-tuned to integrate deep learning into the human-in-the-loop interactive sensemaking pipeline.

Other papers published while Bian has been at the Sanghani Center include the following collaborations with North and other Ph.D. students: “DeepVA: Bridging Cognition and Computation through Semantic Interaction and Deep Learning,” in the proceedings of the IEEE VIS 2019 Workshop on Machine Learning from User Interactions for Visualization and Analytics; and “Evaluating Semantic Interaction on Word Embeddings via Simulation,” presented at the EValuation of Interactive VisuAl Machine Learning Systems Workshop at the same conference.

Bian holds a master’s degree in computer science from Zhejiang University, China, and – along his path to a Ph.D. – earned a second master’s in computer science from Virginia Tech.

“I came to Virginia Tech in 2016 and I think that Blacksburg provides an ideal atmosphere,” said Bian. “There are a lot of great hiking trails around campus and it is a great place to focus on study and research so it helps you find a good academic work-life balance.”

Bian is planning to receive his Ph.D. in 2021 and would like to work in a research industry position after graduation. 


Sanghani Center Student Spotlight: Bipasha Banerjee

When Bipasha Banerjee was looking for a Ph.D. program she had one major criteria: it had to give the highest importance to research. With her continuing passion for knowing more, she wanted to delve deeper into open questions and learn how to solve them.

“The quality of research in computer science at Virginia Tech is unparalleled and professors associated with the Sanghani Center are involved in projects that encompass a large range of real-world issues,” said Banerjee. “I realized this was the right fit for me and, thankfully I was accepted and started an exciting journey of research.”

Advised by Edward Fox, she is a member of the Digital Library Research Laboratory (DLRL), and serves as graduate research assistant for a project funded by the Institute of Museum and Library Services (IMLS)-funded project.

Banerjee’s keen interest in natural language processing goes back to her undergraduate years at West Bengal University of Technology, Kolkata, India, where she graduated with a bachelor’s degree.

In her research, Banerjee works with long documents, especially book-length documents like Electronic Thesis and Dissertations (ETDs). Generally, an ETD, which averages 100 pages, is hosted by the university from which the author graduated. Finding all ETDs related to a particular query requires searching thousands of repositories as there are no global full-text search sites covering the worldwide set of ETDs.

In addition to making ETDs more accessible she aims to add services that make it easier to engage with such book-length documents and tailored specifically to each class of stakeholder.

“Most of the theoretical and applied experimentation is focused on short documents like webpages, journal articles, or papers in conference proceedings. While each of the articles in a journal volume or conference proceedings has its own abstract, there are no summaries for the chapters of an ETD. Aggregating such works in a shared space and performing applied research like segmentation and summarization would prove to be extremely valuable for readers,” Banerjee said.

Banerjee’s focus area was a direct result of her own experience at the beginning of her graduate work at Virginia Tech when she found that most professors urged her to read the theses and dissertations of past graduates in their labs.

“I found reading these documents very useful in understanding the research as opposed to reading a paper, which often, because of page limitations, contain only certain important portions of the research,” she said. “I quickly realized that although the documents contain detailed information, I was only able to parse through the documents quickly if a comprehensive summary was available for sections. Hence, it was a natural fit to work with long documents as my research topic.”

Banerjee said she greatly appreciates the work culture in the Sanghani Center. “It is easy both to approach other students and to seek guidance from faculty members,” she said.

She has collaborated with Fox and another Ph.D. student at the Sanghani Center on the paper, “Summarizing ETDs with deep learning” published by Cadernos BAD 1 (2020).

She is on track to get her Ph.D. in 2023. Her goal after that, Banerjee said, is to remain in academia in a position where she can teach and continue her research


Sanghani Center Student Spotlight: Po-Han Chen

Po-Han Chen, Master’s student in the Department of Computer Science
Graphic is from the paper “RISECURE: Metro Incidents And Threat Detection Using Social Media”

Po-Han Chen, a master’s degree student in computer science, was on the research team for the paper, “RISECURE: Metro Incidents And Threat Detection Using Social Media,” that appeared in the proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) held virtually in December.

The team, which included his advisor Chang-Tien Lu, collaborated with the Washington DC Metropolitan Area Transit Authority (WMATA) to employ an open source system, RISECURE that uses real-time social media mining to aid in the early detection of existing or potential criminal activity within a rail-based/metro system. The system leverages a dynamic query expansion algorithm to keep track of any new emerging information about any particular incident.

In undertaking this research, the team found that existing forms of threat or event detection for rail-based transit systems either do not work in real-time or do not provide complete coverage.

Using social media, which is an area of particular interest to Chen, provides faster access to data that can create beneficial applications.

“This project is a good example of what I like best about being a student at the Sanghani Center — the opportunity to collaborate with other organizations on research which aims to solve real-world problems,” said Chen, who said he was attracted to Virginia Tech and the center because of its work in tackling challenging issues in several fields, including security, sustainability, and public health.

Chen earned a bachelor’s degree in computer science from Yuan-Ze University, Taiwan. He is projected to graduate in 2021 and his goal is to be in a position where he can continue to create useful applications by analyzing large amounts of information.


Subhodip Biswas receives Journeyman Fellowship from Army Research Lab

Subhodip Biswas, Ph.D. student in the Department of Computer Science

Subhodip Biswas is the recipient of the Journeyman Fellowship through the DEVCOM Army Research Laboratory (ARL) Research Associateship Program (RAP) administered by the Oak Ridge Associated Universities (ORAU). This fellowship will provide Biswas the opportunity to work on Bayesian optimization techniques for automated machine learning (AutoML) and robust AI systems.

Journeyman Fellows are selected by an ARL Review Committee based on their overall qualifications and technical proposal addressing specific needs defined by ARL. They work in a unique Army laboratory environment, while interacting with senior ARL scientists and engineers. Fellowships are awarded for one year and may be renewed based upon recommendation of the advisor and availability of laboratory funds.

Biswas, a Ph.D. student at the Sanghani Center, is a computer science major and has earned a graduate certificate in urban computing in the National Science Foundation-sponsored UrbComp program administered through the center. His advisor is Naren Ramakrishnan.


Sanghani Center Student Spotlight: Ola Karajeh

Ola Karajeh, Ph.D. student in the Department of Computer Science
Graphic is from the paper “Predicting Length of Stay for Cardiovascular Hospitalizations in the Intensive Care Unit: Machine Learning Approach”

In her Ph.D. research, Ola Karajeh is investigating efficient solutions to process social media such as Twitter for monitoring public health.

She is particularly interested in the brittleness of these systems, e.g., how non-informational tweets can lead to failure of public health monitoring systems. “Since many institutions report success from building supportive decision making systems based on data collected and processed from sources like Twitter, it is important to identify which posts are non-informational,” she said. 

Her approach involves three phases: Developing graph theoretic formulations; applying deep neural networks and adaptive learning techniques in the detection process; and validating solutions with the Arabic language.

Karajeh earned bachelor’s and master’s degrees in computer science from the University of Jordan. She was drawn to Virginia Tech by its high ranking in the computer science field and for her matched interests with the Digital Library Research Laboratory and its director, Edward Fox, who now serves as her advisor.

“I really like being a part of the Sanghani Center,” she said, citing “the opportunity to be professional, dive into data analysis and machine learning and collaborate with others in the same area.”

Among her most recent collaborations are the papers: “Predicting Length of Stay for Cardiovascular Hospitalizations in the Intensive Care Unit: Machine Learning Approach,” at the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC);  ‘Benchmarking Predictive Models in Electronic Health Records: Sepsis Length of Stay Prediction” at the 2020 International Conference on Advanced Information Networking and Applications Conference; and “Survey of Educational Cyber Ranges” during a workshop at the same conference.

Karajeh is projected to graduate in 2022 and her goal is to work in the academic field as a faculty member in a respected institution.


Virginia Tech researchers garner two major awards in COVID-19 forecasting challenges

Nikhil Muralidhar, a Ph.D. student at the Sanghani Center, is one of the Virginia Tech researchers on the winning DeepOutbreak team.

DeepOutbreak, a team of researchers from Virginia Tech, Georgia Tech, and the University of Iowa, has taken first place in the COVID-19 Symptom Data Challenge.

The competition explores how Facebook symptom survey data can enable earlier detection and improved situational awareness of COVID-19 and flu outbreaks that can help both public health authorities and the general public make better decisions.

The first place award, announced by Catalyst @Health 2.0 in late December, and the team’s work will be featured on the Facebook Data for Good blog. Facebook was one of the sponsors of the challenge. Click here to read more about the challenge.


$10M GIFT WILL HELP FUEL DISCOVERIES AT INNOVATION CAMPUS

Mehul and Hema Sanghani. Photo courtesy of the Sanghanis.

Virginia Tech’s growing impact in the greater Washington, D.C., metro area will receive a significant boost thanks to a multimillion-dollar gift from Octo founder and CEO Mehul Sanghani ’98 and his wife, Hema Sanghani ’99.

The couple’s $10 million gift primarily supports the newly renamed Sanghani Center for Artificial Intelligence and Data Analytics, which will be headquartered in the first academic building at the university’s Innovation Campus in Alexandria, Virginia. A majority of the gift is endowed to support recruiting, research, and fellowships at the center, which has operated since 2011 and was formerly known as the Discovery Analytics Center. Funding will also be allocated toward a Sanghani Center scholars program which will afford scholarship opportunities to underrepresented minorities to pursue graduate degrees with a focus on artificial intelligence.

Click here to read more about their gift.


Thomas Jefferson High School student’s DAC summer internship leads to his first publication — Jason Wang presents paper at IEEE International Conference on Big Data

Jason Wang, Thomas Jefferson High School student
Graphic is from Wang’s research on “SOSNet: A Graph Convolutional Network Approach to Fine-Grained Cyberbullying Detection.”

What was Jason Wang’s most important takeaway as a research intern at the Discovery Analytics Center last summer?

Reflecting on his experience, Wang, a senior at Thomas Jefferson High School for Science and Technology in Alexandria, Virginia, said that the two most valuable things he learned are first, while some of the approaches you try do not work as planned, they could serve as stepping stones to the final model and second, “speak up and be unafraid of sharing failures so as not to get stuck in a single direction.”

Wang, whose interest lies in social media mining and natural language processing, worked under the supervision of  Chang-Tien Lu, professor of computer science, and Lu’s Ph.D. student Kaiqun Fu.  

“Jason actively joined our weekly group seminar discussions, and collaborated with my senior students on advanced research topics in deep learning and social media analysis,” said Lu. “He wrote a high-quality research paper which is an important step towards active anti-cyberbullying measures for the increasingly digital world that we all live in and contributes to my ongoing research on social media mining and event detection.”

Lu encouraged Wang to submit the paper, “SOSNet: A Graph Convolutional Network Approach to Fine-Grained Cyberbullying Detection,” to the 2020 IEEE International Conference on Big Data. It was accepted and Wang presented it earlier this month.

During the research process, Wang found few publicly available datasets with precise enough cyberbullying labels to train his algorithm. He overcame this challenge by labeling thousands of tweets by modifying dynamic query expansion (DQE) to enable semi-supervised online generation of specific types of cyberbullying tweets.

“I was able to do this by iteratively growing a small set of seed tweets via Twitter queries,” Wang said.

The IEEE International Conference on Big Data was held remotely but Wang answered questions live.

“I thought that being questioned by other researchers would be daunting, but in the heat of the moment it was actually pretty exciting that they were taking interest into my work,” Wang said. “I am grateful for my mentors’ guidance and support throughout this whole process.”

(Note: If you are a high school student with an interest in pursuing an internship at the Discovery Analytics Center, please contact Wanawsha Shalaby with a resume and areas of interest.)


Congratulations 2020 Fall Graduates!

Among the graduates at Virginia Tech’s 2020 Fall commencement are five Ph.D.’s and six master’s students at the Discovery Analytics Center.

“This year has certainly been a challenging one but our students have persevered. Remotely, they completed required courses and successfully finalized and defended their research,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the center. “We are very proud of all they have accomplished and wish them continued success in their new professional positions.”

Ph.D. graduates

Saurabh Chakravarty, advised by Edward Fox, is receiving a Ph.D. in computer science. His research focus is in the area of text analytics related to comprehending information contained in a text and processing/summarizing/classifying it for other downstream tasks or use cases. The title of Chakravarty’s dissertation is “Summarizing Legal Depositions.” He has joined JWPlayer, New York City, as a senior software engineer.

Jinwoo Choi, advised by Jia-Bin Huang, is receiving a Ph.D. in electrical and computer engineering. His research interest in computer vision and machine learning lies more specifically in understanding what is going on in a video. The title of his dissertation is “Action Recognition with Knowledge Transfer.”  (Updated 2/16/21 — Choi will join the Department of Computer Science and Engineering, Kyung Hee University, South Korea, as an assistant professor in March.)

Ziqian Song, advised by Edward Fox, is receiving a Ph.D. in computer science. Her primary research interests lie at the intersection of machine/deep learning and business intelligence. The title of her dissertation is “The Impact of Operational Crisis on Firm Equity Value: An Event-driven Approach.”  Song is joining the University of Scranton as an assistant professor in the Department of Operations and Information Management, Kania School of Management.

Rongrong Tao, advised by Naren Ramakrishnan, is receiving a Ph.D. in computer science. Her research interest lies in forecasting population-level events. The title of Tao’s dissertation is “Anomalous Information Detection in Social Media.”

Amirsina Torfi, advised by Edward Fox, is receiving a Ph.D. in computer science. His research aimed at generating high-fidelity medical synthetic data with the power of AI to remedy the privacy issues of the healthcare domain. The title of Torfi’s dissertation is “Privacy-Preserving Synthetic Medical Data Generation with Deep Learning.”  He has founded his own company, Instill AI.

Master’s graduates

Amr Aboelnaga, advised by Edward Fox, is receiving a master’s degree in computer engineering. His research interest is in classifying detected ultrasonic vocalizations to help autotomize the classification process for other domains such as neuroscience. Aboelnaga’s thesis is titled “USV Classification: an adaptation from DeepSqueak.”


Mohannad Al Ameedi, advised by Chang-Tien Lu, is receiving a master’s degree in computer science. He is a software engineer at Microsoft and was a part time student at Virginia Tech. Ameedi has also earned a graduate certificate in data analytics. His research interests are machine learning, deep learning, and information storage and retrieval.

Sampanna Kahu, advised by Edward Fox, is receiving a master’s degree in computer engineering.  His research focus is on developing methods for effectively extracting figures and tables from scanned Electronic Theses and Dissertations (ETDs), thereby making their downstream usage easier. His thesis is titled “Figure Extraction from Scanned Electronic Theses and Dissertations.” Kahu has joined Amazon, Inc. as a software development engineer 2.

Maanav Mehrotra, advised by Edward Fox, is receiving a master’s degree in computer science. His research involves developing automated techniques to convert question answer pairs into their canonical/declarative form to aid in extracting insights from the given document and assist in summarization. His thesis is titled “Generating Canonical Sentences from Question-Answer Pairs of Deposition Transcripts.”

Palakh Mignonne Jude, advised by Edward Fox, is receiving a master’s degree in computer science. Her research focus is improving classification of Electronic Theses and Dissertations (ETDs) that enables other researchers to better utilize this information. Jude’s thesis is titled “Increasing Accessibility of Electronic Theses and Dissertations (ETDs) Through Chapter-level Classification.” Jude has joined Bloomberg as a software engineer.

I-Hsuan (Lambert) Tao, advised by Chang-Tien Lu, is receiving a master’s degree in computer science. His research focus is on connecting Twitter posts and navigation application in order to reduce the chances of information inequivalent. The title of Tao’s thesis is “Twitter based Traffic Event Detection System Based On Land Use.” Tao is joining Amazon.


World Wildlife Fund partners with Discovery Analytics Center on automated system to help save forests

Aerial view of Amazon deforestation, municipality of Calamar, Guaviare Department, Colombia. The “buffer zone” around Chiribiquete National Park, Colombia is being deforested at an alarming rate, due to land grabbing and cattle ranching, especially in areas newly “opened up” as a result of the peace process. Photo © Luis Barreto / WWF-UK

Nearly half the world’s forests are under threat of deforestation and forest degradation.

Forests are at most risk of being destroyed by degradation — slashed trees, bare clearings, newly formed trenches and water gullies, and water clouded by eroding soil — which often leads to deforestation. Forest degradation has an even greater environmental, economic, and social impact because it not only affects the structure and function of a forest, but also lowers its capacity to provide goods and ecosystem services to help keep air and water clean, provide wildlife and humans with shelter and food, and capture carbon. More than three-quarters of the world’s land-based species live in forests, and over 1.5 billion people rely directly on forests for their livelihoods. Click here to read more.


DAC Student Spotlight: Akshita Jha

Akshita Jha, DAC Ph.D. student in the Department of Computer Science

Akshita Jha’s primary research focuses on how to prevent automated machine learning models from exacerbating existing biases.

“As an example,” Jha said, “the commercial algorithm, COMPAS, used by judges and officers across the United States to assess a defendant’s likelihood to re-offend has been shown to discriminate unfairly against African American defendants.”

Another example, she said, is a nationwide healthcare risk-score algorithm that provides healthcare decisions for over 70 million patients in the United States. It suggests better health resources for some demographics when compared to those suggested for others.

“Models like these unfairly discriminate against already marginalized social groups and the goal of my research is to build models providing fairness, accountability, and transparency that can help prevent such discrimination,” she said.

How did Jha become interested in this area of research?

“A couple of years ago I was presenting my work in an open-source conference and noticed the abysmal number of women in a conference attended by hundreds of people from across the world,” she said. “This left a jarring impression on me and I became extremely aware of the pervasiveness of sexism. Being a computer science major, I started thinking of ways in which machine learning and natural language processing could be used to analyze social data and mitigate this issue. That motivated me to delve deeper into this field.”

Jha, who holds both a bachelor’s degree in computer science and a master’s degree by research in natural language processing from IIIT-Hyderabad, India, is a Ph.D. student in computer science advised by Chandan Reddy.

What Jha likes best about being a student at the Discovery Analytics Center is the opportunity for discussions with other Ph.D. students on different research topics.

“Not only are they interesting, but also provide a varied perspectives to the problem at hand,” she said. 


This past summer Jha interned with the Interdigital AI Lab in Palo Alto, California. Her work involved building a human-interpretable long document comparison model.

She is projected to graduate in 2023.




DAC Student Spotlight: Yi-Chun Chang

Yi-Chun Chang, DAC master’s student in the Department of Computer Science

Graphic is from the paper, “RIDE-SECURE: Metro Security Incidents And Threat Detection Using Social Media”

Yi-Chun Chang, who holds a bachelor’s degree in business information management from National Taiwan University, was drawn to pursue a master’s degree in computer science at Virginia Tech by its reputation for quality research and the prospect of working Chang-Tien Lu, now his advisor.

“Being a student at the Discovery Analytics Center is amazing,” said Chang. “We have plenty of resources and so many great opportunities to collaborate.”

Chang’s current project with Lu is a collaboration  with a Maryland firm funded by the Washington Metropolitan Area Transit Authority (WMATA). The team is developing an advanced spatiotemporal event detection system of several layers to deal with data preprocessing, information extraction, threat level analysis, and visualization and extract security-related information from social media contents that can help metro police improve security on trains and at metro stations.

A collaborative paper relative to the WMATA project, “RIDE-SECURE: Metro Security Incidents And Threat Detection Using Social Media,” has been submitted to a December conference for review.

Chang’s interest in using social media analytics for detecting threats began as an undergraduate, inspired by some of the projects he worked on and participating in hackathons.

This past summer, Chang was a full-stack software engineer intern at Walmart Global Tech where his responsibilities included redesigning and implementing responsive enterprise web applications and role-based solutions of feature toggle using React.js, Node.js, GraphDB, and Azure; and establishing coding standards across the team with ESLint, customized error code, and API versioning, achieved the team goal of 90 percent for API Test Coverage.

Projected to graduate in May 2021, Chang said his ultimate goal “is to become an industrially-driven professional who develops products that fulfill the need for reliable and secure software applications or systems.”


Discovery Analytics Center welcomes new faculty member Lifu Huang

The Discovery Analytics Center continually brings together computer scientists, engineers, and statisticians to meet the research and workforce needs of today’s data-driven society. This Fall, DAC welcomes new faculty member Lifu Huang. He has joined the Virginia Tech Department of Computer Science as an assistant professor, having earned his Ph.D. in computer science at the University of Illinois Urbana-Champaign.

Huang’s primary research interests are in the fields of natural language processing, machine learning and artificial intelligence. He is specifically interested in building efficient models and benchmarks that can encourage machines to perform human-level intelligence.

His current projects include extracting structured knowledge with limited supervision; natural langue understanding and reasoning; natural language generation and representation learning.

As a graduate student, he was a research intern at Microsoft Research Asia; IBM Watson Research; U.S. Army Research Center; and Allen Institute for Artificial Intelligence (AI2), where he was recipient of the 2019 AI2 Fellowship.

Huang received a bachelor’s degree in software engineering from Northwestern Polytechnical University, China, in 2011 and a master’s degree in computer science from Peking University in 2014.

 


Chang-Tien Lu conducts research for U.S. Army Corps of Engineers to help detect insider threats

Chang-Tien Lu, professor in the Department of Computer Science and associate director of DAC

The challenge of detecting threats in war zones is even greater when assessing the possibility of an insider attack.

“Seemingly innocent insiders can become dangerous due to a number of circumstances including  personal relationships and geospatial environments,” said Chang-Tien Lu, a professor in the Department of Computer Science, associate director of the Discovery Analytics Center, and a faculty member in the National Science Foundation- sponsored UrbComp program 

“For example,” Lu said, “an Afghan soldier aligned with U.S. troops or a civilian working on a military base could be influenced by a friend with Taliban ties and, over time, come to pose an increased threat.”

Lu has received $90,000 from the U.S. Army Corps of Engineers to design a machine learning-based intelligent system that combines entity and environmental information into a non-linear approach to evaluate insider threat level and identify dangerous scenarios.

Entity information includes demographic and social media data and environmental data includes location, physical appearances of an area, and distance and connectivity,

In designing and testing the system, Lu will utilize a number of publicly available data sources. These include: CycloMedia GlobeSpotter; Google Street View; satellite and aerial LiDAR data; and economic indicators like Gross Domestic Product (GDP), unemployment rates, gross savings rate, and World Bank data, and crime record data obtained from government open data projects.

In previous studies, Lu said, the incidence of crimes based on race, ethnicity, or religious bias have proved relative to the danger level as have environmental elements, like the number of  reported broken windows, thefts, and assaults.

Ph.D. student Lei Zhang is working on the 14-month project with Lu, who is his advisor.

Their results will be displayed in the form of percentages representing the likelihood of an imminent threat.

Lu said the new system should enhance the fundamental methodologies of data mining from heterogeneous spatial contexts and contribute to new computational models for threat discovery.

“We believe it will be flexible enough to be adapted to various military and civil contexts,”  said Lu.


Chang-Tien Lu working with Maryland firm on advanced event prediction system to improve metro security

Chang-Tien Lu, a professor in the Department of Computer Science and associate director of DAC

Chang-Tien Lu, a professor in the Department of Computer Science, associate director of the Discovery Analytics Center, and a faculty member in the National Science Foundation- sponsored UrbComp program  has teamed up with Schneider Electric Buildings Critical Systems, Inc. (SEBCSI)  on a project entitled “Advanced Analytics for High-Performance Metro Security Monitoring,” funded by the Washington Metropolitan Area Transit Authority (WMATA)

SEBCSI, located in Columbia, Maryland, provides electronic security solutions, including security access control and closed circuit television (CCTV) for commercial and government applications.

Lu and a team of graduate students will develop an advanced spatiotemporal event detection system of several layers to deal with data preprocessing, information extraction, threat level analysis, and visualization and extract security-related information from social media contents that can help metro police improve security on trains and at metro stations.

“With the popularity of mobile devices, more public transport riders are using Twitter to report observations of potential threats, ongoing crimes, and other metro incidents. Social media serves as a good supplemental data source because tweets can cover multiple types of security issues — the most common being fight, stealth, and robbery — that are typically posted by a witness either during an ongoing crime or by the victim immediately after a crime,” said Lu.

Another advantage of social media, said Lu, is that security issue related tweets usually include station names and very accurate timestamps.

The researchers’ new system will mine collected security issues from tweet collections and visualize a crime hotspot of metro stations on a geospatial map based on location information while simultaneously representing an auto-summarized station-wise report.

“This data can help metro police make better decisions in deploying limited resources,” Lu said.

DAC students from the Department of Computer Science who are working with Lu on this project are Ph.D. student Jianfeng He and master’s students Omer Zulfiqar, Yi-Chun Chang, and Po-Han Chen.

The project builds on Lu’s previous research in intelligent transportation systems, “Steds: Social Media based Transportation Event Detection with Text Summarization,”

“A Search and Summary Application for Traffic Events Detection Based on Twitter Data,”

“Determining Relative Airport Threats from News and Social Media,” and

 “TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration.”

 

 

 


DAC students working virtually at summer internships across the country

DAC Ph.D. student Chidubem Arachie is working remotely as an intern at Google Research.

A national pandemic that forced the closing of physical offices has not stopped graduate students at the Discovery Analytics Center from working remote internships at companies, research laboratories, and other institutions across the country. For many students, summer internships help further their own research as they gain real world experience.

Following is a list of DAC students and the work they are doing for the next few months:

Chidubem Arachiea Ph.D. student in computer science, is a research intern at Google Research in Mountain View California. He is working on generative modeling for 3D shapes. His advisor is Bert Huang.

John Aromando, a Ph.D. student in computer science, is an intern at Graf Research in Blacksburg, working on utilizing natural language processing to support the software verification process. His advisor is Edward Fox.

Hongjie Chen, a Ph.D. student in computer science, is a data science research intern at Adobe in San Jose, California. He is on the Cloud Technology Team, researching cloud resource allocation strategy. His advisor is Hoda Eldardiry.

Nurendra Choudhary, a Ph.D. student in computer science, is an applied science intern with the Amazon Search Team in Palo Alto, California. He is working on representation learning of products by leveraging the heterogeneous relations between them. His advisor is Chandan Reddy.

Chen Gao, a Ph.D. student in electrical and computer engineering, is a research intern at Google in Mountain View, California. He is working on improvements to the portrait mode on the Google Pixel phone. His advisor is Jia-Bin Huang.

Akshita Jha, a Ph.D. student in computer science, is a research intern in the Interdigital AI Lab in Palo Alto, California. Her work involves building interpretable natural language processing models. Her advisor is Chandan Reddy.

Prerna Juneja, a Ph.D. student in computer science, is an intern at the Information Science Institute at the University of Southern California with Emilio Ferrara, assistant research professor and associate director of Applied Data Science in the Department of Computer Science. She is investigating the spread of COVID-19 related conspiracy theories on Twitter. Her advisor is Tanushree Mitra.

You Lu, a Ph.D. student in computer science, is a research intern at NEC Labs America in Princeton, New Jersey, working on sequence labeling for signals in fibers. His advisor is Bert Huang.

Shruti Phadke, a Ph.D. student in computer science, is doing a research internship with James Pennebaker, a professor in the Department of Psychology at the University of Texas at Austin. She is studying online communities, their social processes, and behaviors. Her advisor is Tanushree Mitra.

Aarathi Raghuraman, a master’s degree student in computer science, is an intern at GlaxoSmithKline (GSK), working with the Digital, Data, and Analytics team to maximize process yield in upstream biopharm manufacturing. She is advised by Lenwood Heath.

Esther Robb, a master’s degree student in electrical and computer engineering, is a research intern at Google working with a team in San Francisco on reinforcement learning. Her advisor is Jia-Bin Huang.

Mandar Sharma, a master’s student in computer science, is working as a machine learning intern with Toyota Motors North America, specifically the Toyota Racing Development (TRD) branch, to help NASCAR drivers make better decisions when they are racing. His advisor is Naren Ramakrishnan.

Aarohi Sumant, a master’s student in computer science, is an intern at Amazon. She is working with the Kindle Marketing Team to develop machine learning techniques for book recommendations based on cross user activities as well as single-user activities on different Amazon platforms. Her advisor is Edward Fox.

Afrina Tabassum, a Ph.D. student in computer science is a data science intern in the Data Science for The Public Good (DSPG) program at the Biocomplexity Institute’s Social and Decision Analytics Division (SDAD) at the University of Virginia. She is working on projects that address state, federal, and local government challenges around critical social issues relevant in the world today. Her advisor is Hoda Eldardiry.

Mia Taylor, a senior undergrad in computer science, is interning at Amazon Web Services in the Route 53 (DNS) service. Her advisor is Hoda Eldardiry.

Sirui Yao, a Ph.D. student in computer science, is an intern at Google, working on tag prediction for recommender systems through learning items and tags embeddings. Her advisor is Bert Huang.

Shengzhe Xu, a Ph.D. student in computer science, is interning at Facebook Ads Core ML, working on attention-based time sequential embedding aggregation. Xu’s advisor is Naren Ramakrishnan.

Ming Zhu, a Ph.D. student in computer science, is interning at Amazon. She is an applied scientist intern for Amazon Alexa AI, working on conversational query representation learning. Zhu’s advisor is Chandan Reddy.

Yuliang Zou, a Ph.D. student in electrical and computer engineering, is working on learning with less/weaker annotations at Google. His advisor is Jia-Bin Huang.


Students focus on COVID-19 impacts on sustainability, education, and society

Clockwise from top left: UrbComp students Nikhil Muralidhar, Joshua Detwiler, Whitney Hayes, and Shane Bookhultz

Students in the urban computing graduate certificate program gave their group presentations via Zoom at the end of semester 2020 Spring Retreat, focusing on the very thing that led to this virtual format — COVID-19.

The students were charged with taking a look at the pandemic’s impact beyond health — such as economic outcomes, urban design, and interpersonal and online relationships — by Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science and director of the Discovery Analytics Center, which administers the National Science Foundation-sponsored multidisciplinary program. Click here to read more about the UrbComp Spring Retreat.


Three funded fellows earn UrbComp graduate certificate this spring

Stacey Clifton (left), Michelle Dowling (center), and Moeti Masiane (right)

Three funded UrbComp fellows, Stacey Clifton, Michelle Dowling, and Moeti Masiane, earned the graduate certificate in urban computing this spring. The certificate is offered through the National Science Foundation-sponsored multidisciplinary UrbComp Program administered by the Discovery Analytics Center.

Clifton, advised by James Hawdon and B. Aditya Prakash, graduated with a Ph.D. in sociology and Dowling, advised by Chris North and Mike Horning, with a Ph.D. in computer science.

Masiane, advised by Chris North and Eric Jacques, will complete his Ph.D. in computer science during the summer semester.

Dowling and Masiane were also students at the Discovery Analytics Center.

Clifton was drawn to the program both for its multidisciplinary approach and as a way to advance her quantitative skillset.

“I wanted to challenge myself to do something out of the norm and UrbComp provided me with the quantitative skills to be the first in my department to complete a comprehensive examination in advanced quantitative methods,” she said.

“I was able to further apply this skillset to my dissertation research to add a novel component to the study of policing research,” said Clifton. Her dissertation is titled “Coping isn’t for the Faint of Heart: An Investigation into the Development of Coping Strategies for Incoming Police Recruits.”

Clifton, who is joining Radford University as an assistant professor in the Department of Criminal Justice, said she would “100 percent recommend this program as a vital component to graduate studies.”

For Dowling, the program “helped hone the audience for my research to those performing truthfulness determinations based on a given claim,” she said. “This allowed me to focus on how I described my research, making it easier for others to understand its impacts.”

Dowling’s dissertation is titled “Semantic Interaction for Symmetrical Analysis and Automated Foraging of Documents and Terms.”

She said that her first-hand experience of collaborating with others outside her field of study has shown her how beneficial wide collaboration can be.

“I fully intend to continue seeking such collaboration opportunities,” said Dowling, “and I hope to make connections with professors in different departments as I establish myself as an assistant professor at Grand Valley State University.

For Moeti, whose dissertation is on “Insight Driven Sampling for Interactive Data Intensive Computing,” the program expanded his interest in analyzing large data sets and telling stories about such data to include analyzing large data sets related to urban cities.

“UrbComp class projects allowed me to acquire practical experience with data analysis and machine learning and the data modeling skills I have acquired will surely help me in future data analysis work,” said Moeti. “But I think the most important thing I learned in the program was the ethical aspect of data analytics.”

The UrbComp program is open to Virginia Tech master’s and Ph.D. students in any discipline located in Blacksburg or the greater Washington D.C. campus.

For more information contact program coordinator Wanawsha Shalaby.

 


Congratulations to DAC’s 2020 Spring and Summer Graduates!

Among Virginia Tech graduates celebrating their achievements today include four Ph.D. and five master’s students at the Discovery Analytics Center.

Four Ph.D. students and one master’s student plan to complete degrees during the summer.

“The thoughtful and impactful research our students have engaged in while pursuing their graduate degrees has been recognized by many major academic conferences and is testament to their hard work,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the center.

“We are always very proud of our graduates but especially so this year as they have had to persevere through some very unusual circumstances to achieve their goals,” Ramakrishnan said. “We wish them continued success as they venture into new career challenges at universities, research laboratories, and businesses.”

Ph.D. Spring graduates

Bijaya Adhikari, advised by B. Aditya Prakash, is receiving a Ph.D. in computer science. His research interests are data science and machine learning for large networks and data driven epidemiology. The title of his dissertation is “Domain-based Frameworks and Embeddings for Dynamics over Networks.” Adhikari is joining the Department of Computer Science at the University of Iowa in the fall as a tenure track assistant professor.

Tyler Chang, advised by Layne Watson, is receiving a Ph.D. in computer science. His research interests are numerical approximation, optimization, algorithms, parallel computing, data science, and scientific computing. The title of his dissertation is “Mathematical Software for Multi-objective Optimization Problems.” Chang is joining the Mathematics and Computer Science Division at Argonne National Laboratory in Chicago, Illinois. Specifically, he will work in the Laboratory for Applied Mathematics, Numerical Software, and Statistics as a postdoctoral appointee, a group he previously interned with.

Michelle Dowling, advised by Chris North and Mike Horning, is receiving a Ph.D. in computer science. She is also receiving a graduate certificate in urban computing, a National Science Foundation-sponsored program administered through DAC. Dowling’s research interests are human-computer interaction, data analytics, information visualization, and interactive data visualization. The title of her dissertation is “Semantic Interaction for Symmetrical Analysis and Automated Foraging of Documents and Terms.” Dowling is joining Grand Valley State University, her alma mater, as an assistant professor.

Mohammad Raihanul Islam, advised by Naren Ramakrishnan, is receiving a Ph.D. in computer science. His research interests are social network/media analysis, deep learning, and graph neural network. The title of his dissertation is “Detecting and Mitigating Rumors in Social Media.”  Islam is joining Amazon, Inc., as an applied scientist. 

Liuqing Li, advised by Edward Fox, is receiving a Ph.D. in computer science. His research interests are digital library, social analysis, machine learning, and deep learning. The title of his dissertation is “Event-related Collections Understanding and Services.” Li is joining Yahoo! as a research scientist.

Master’s Spring Graduates


Arjun Choudhry
, advised by Naren Ramakrishnan, is receiving a master’s degree in computer science.  His research interests are narrative generation, blockchain technologies. His thesis is titled “The Art of Simplifying Graph Interpretation: Narrative Generation Using Causal Exploration of Directed Graphs.” Choudhry is joining Amazon, Seattle, as a software development engineer.

Jeffrey McCullen, advised by Chandan Reddy, received a master’s degree in computer science. His research interests are machine learning and data analytics in healthcare, and software engineering.  The title of his thesis is “Predicting the Effects of Sedative Infusion on Acute Traumatic Brain Injury Patients.”

Joseph Messou, advised by Jia-Bin Huang, is receiving a master’s degree in computer engineering. His research interests are computer vision and machine learning, efficient training methods for networks, and cybersecurity. The title of his thesis is “Handling Invalid Pixels in Convolutional Neural Networks.”  In the fall, Messou will be a Ph.D. student in computer engineering at the University of Maryland, College Park.

Shih-Yang Su, advised by Jia-Bin Huang, is receiving a master’s degree in computer engineering. His research interests are machine perception, visual representation learning, and reinforcement learning. His thesis is titled “Learning to Handle Occlusion for Motion Analysis and View Synthesis.” In the fall, Su will be a Ph.D. student in computer science at the University of British Columbia, where his research will focus on learning and understanding human motion for motion synthesis and character animations.

Ming Wang, advised by Chris North, is receiving a master’s degree in computer science. Her research interests are visual analytics and information visualization. Her thesis is titled “Bridging Cognitive Gaps Between User and Model in Interactive Dimension Reduction.” Wang is joining Salesforce as a software engineer.

Summer Ph.D. graduates

Zhiqian (Danny) Chen, advised by Chang-Tien Lu, will complete his Ph.D. in computer science. Chen’s research interests are graph mining, urban computing, network science. The title of his dissertation is “Graph Neural Networks: Techniques and Applications.” Chen will join the Computer Science and Engineering Department at Mississippi State University as assistant professor.

Tianyi Li, advised by Chris North, will complete her Ph.D. in computer science. Her research interests include developing systems for computer-supported cooperative work and devising visual analytics tools with user-centered design to combine and coordinate human and artificial intelligence in broader, real-world sensemaking processes. Her dissertation is titled “Solving Mysteries with Crowds: Supporting Crowdsourced Sensemaking with a Modularized Pipeline and Context Slices.”  Li will be joining Loyola University in Chicago as assistant professor.

Thomas Lux, advised by Layne Watson, will complete his Ph.D. in computer science. His research interests are approximation, optimization, and mathematical software. His dissertation is titled “Interpolants and Error Bounds for Modeling and Predicting Variability in Computer Systems.”

Moeti Masiane, advised by Chris North, will complete his Ph.D. in computer science. He has received a graduate certificate in urban computing, a National Science Foundation-sponsored program administered through DAC. Masiane’s research interests include information visualization, data modeling, insight, sampling, and perception modeling. The title of his dissertation is “Insight Driven Sampling for Interactive Data Intensive Computing.”

Summer master’s graduate

Milad Afzalan, advised by Hoda Eldardiry, will complete his master’s degree in computer science. His research interests include machine learning, pattern recognition, smart grid, and energy efficiency. The title of his thesis is “Household electricity load shape segmentation from smart meter data based on temporal patterns and power magnitude.” Afzalan, who will also be receiving a Ph.D. from Virginia Tech in civil engineering, will join ENGIE as a data scientist.


DAC Student Spotlight: Gopikrishna Rathinavel

Gopikrishna Rathinavel, DAC M.S. student in the Department of Computer Science

Gopikrishna Rathinavel was introduced to machine learning through the biotechnology courses he took as an undergraduate.

“Eager to learn more, I began attending lectures by industry experts in machine learning,” Rathinavel said. “Soon I was captivated by the potential that machine learning offers as a discipline. It can add valuable insights in any domain where there is some data to exploit.”

He also began following the work of Discovery Analytics Center Director Naren Ramakrishnan, who is now his advisor, and other Virginia Tech faculty.

“What intrigued me the most was the use of satellite images of hospital parking lots to monitor disease trends,” he said. “It was novel research and something that I was keen to learn more about.”

Rathinavel graduated from the Indian Institute of Technology (IIT) in Madras with a dual bachelor and master’s degree in technology and worked as a software engineer for four years before starting the master’s program in Virginia Tech’s Department of Computer Science in Fall 2019 and joining DAC.

“Cross collaboration and plenty of pioneering research between departments at Virginia Tech means more opportunities to tie in my knowledge of biotechnology with machine learning,” said Rathinavel.

“At DAC I am able to work on a number of projects in different fields like systems biology, social media analytics, and urban computing that tackle global-scale problems,” he said. “This provides a unique experience.”

Another plus in being part of the DAC community, Rathinavel said, is that everyone has something to offer. “It is a very natural learning atmosphere where we share our knowledge and are always ready to lend a helping hand and support each other.”

His current research includes generating predictive models based on events that occurred in the past, using data extracted from news articles relevant to specific corporate events such as business contracts, cyberattacks, and executive resignations.

“Patterns and anomalies found from these events could go a long way in helping experts in the industry make informed decisions,” he said.

Projected to graduate in 2021, he would like to work in industry in a research role that utilizes machine learning.

 

 

 

 

 

 

 

 


DAC Student Spotlight: Omer Zulfiqar

Omer Zulfiqar, DAC master’s student in the Department of Computer Science.

Graphic is from the paper, “RIDE-SECURE: Metro Security Incidents And Threat Detection Using Social Media”

After graduating from Virginia Tech in December 2018 with a bachelor of science degree in electrical engineering and a minor in computer science, Omer Zulfiqar moved to northern Virginia to be closer to his family. He was also in close proximity to the university’s location in the greater Washington, D.C. area.g

In Fall 2019, he began pursuing a master’s degree in computer science and once again, chose Virginia Tech, this time at the Falls Church campus.

“Virginia Tech is a world renowned university in the field and at the Discovery Analytics Center I am able to work on interdisciplinary collaborations guided by incredible faculty, like my advisor Dr. Chang-Tien Lu, who are doing some amazing research work in the fields of artificial intelligence, machine learning, and data mining,”  Zulfiqar said.

Zulfiqar’s interest in machine learning was sparked during his senior year when he took courses involving data analytics and artificial intelligence.

“I was working with natural language processing, doing small projects with chatbots and also touching the surface of deep learning,” he said. “The idea of how you can analyze tons of data, extract value and glean insight from it, and then use it to develop a model that could give you predicted results was always very intriguing to me. I didn’t just want to work on something that people would use, I wanted to develop something that could help people. Seeing the potential that it has to help tackle some of the world’s most challenging problems I decided to focus on machine learning.

With help from Lu, his first year of graduate work has afforded him an opportunity to explore a number of different research topics including detecting and monitoring events by analyzing social media data; detecting threats within rail-based transit systems; and developing knowledge graphs to analyze and monitor epidemics.

Zulfiqar collaborated with Lu and other DAC students on the Washington Metropolitan Area Transit Authority (WMATA) study, “RIDE-SECURE: Metro Security Incidents And Threat Detection Using Social Media,” which developed a model using data from Twitter to help identify and detect threats within a rail based public transit system, using convolutional neural networks and dynamic query expansion. In February, the team submitted the paper to a major conference and is awaiting approval.

He is projected to receive his master’s degree in Spring 2021 and is considering two options upon graduation: an industry position as a software or machine learning engineer or continuing his education in a Ph.D. program.

“If I decide to pursue a Ph.D., it would again be at Virginia Tech because I would definitely want to continue my research with Dr. Lu at DAC,” Zulfiqar said.

 


Research award aims to develop new algorithms for information extraction and understanding from scholarly literature

Naren Ramakrishnan, Director of DAC and Professor in the Department of Computer Science

The Discovery Analytics Center has received a research award from the Center for Security and Emerging Technology (CSET) at Georgetown University to support data-informed analysis for policymakers  concerning emerging technologies and their security implications. DAC will develop methods to extract novel insights at scale from full-text analytics of publications to better understand emerging technologies and their prevalence, spatial and temporal trends, and relationships.

“Algorithmic components developed by DAC will go into a high-performance pipeline that enables inspection of extracted patterns as well as the lineage of data transformations underlying the patterns,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and DAC director, who is the principal investigator for the project.

Ramakrishnan’s team at DAC — which includes senior research associate Patrick Butler; research associate Brian Mayer; and three Ph.D. students — will develop a machine learning framework based on weak supervision to process full-text AI publications into extracted structured fields, such as information on computational platforms utilized, language and library dependencies, compute time, research methods, objective tasks, and links to source code and data resources.

The initial focus will be on arXiv as researchers evaluate and assess progress followed by extraction from China National Knowledge Infrastructure (CNKI) literature, which provides full-text articles from more than 8,000 Chinese journals covering natural sciences, engineering, technology, agriculture, medicine, and selected topics in economics and social sciences.

This project is providing DAC with the opportunity to build on its prior work in extracting information from news articles about civil unrest events.  It will also be informed by DAC’s experience with automated extraction of epidemiological line lists from disease reports, which is used to develop custom word embeddings aimed at recognizing the typical language patterns in how computational details are described in the scholarly literature.

“This project brings together machine learning, computational linguistics, and human-computer interaction capabilities to extract features at scale. The information we extract will be mapped over time to help identify key trends and potential gaps that can support analysts and policy makers at the CSET,” said Ramakrishnan.

“We are looking forward to seeing how this innovative work can help inform CSET’s analysis as we strive to inform the future of AI policy,” said Dewey Murdick, director of Data Science at CSET.

 

 

 


DAC Student Spotlight: Andreea Sistrunk

Andreea Sistrunk, DAC Ph.D. student in Department of Computer Science

Graphic is from the paper “REGAL: A regionalization framework for school boundaries”

When Andreea Sistrunk started taking classes at Virginia Tech in the fall of 2014 she had left her job as a full time teacher in northern Virginia to devote more time to her two young daughters, ages three and seven.

“It was becoming more difficult for me to hold a full time job and be a good mother so I chose to take a break from work,” Sistrunk said. “I used a sort of ‘mom’s night out’ to enroll in a graduate course at Virginia Tech because I really missed learning new things.”

Sistrunk was drawn to computer science. She held a bachelor of science degree in engineering with a minor in childhood education from University Polytechnica in Bucharest, Romania, and was a licensed teacher for K-12 and Advanced Placement classes in mathematics, computer science, and technology.

From that course, she eventually applied to the Computer Science program and earned a master’s degree with a concentration in data analytics in Fall 2019. Currently, Sistrunk is in the Ph.D. program and a student at the Discovery Analytics Center, where her advisor is Naren Ramakrishnan.

Sistrunk has gone back to work full time as a research scientist in a laboratory outside of the university that focuses on geospatial research. She believes that the combination of work and furthering her education has added a competitive edge to her work as a data scientist.

“I am grateful for the rigor and world class education I am receiving,” Sistrunk said. “My advisor has helped me refine my research direction while I take classes in data science, ethics, and artificial intelligence.”

Sistrunk cited other DAC faculty instrumental in her learning experience. Among them are Chandan Reddy, whose class on artificial intelligence “not only gave me exposure to the newest algorithms in machine learning, reinforcement learning, and deep learning, but taught me how to implement them. It was a super tough but so worth every single minute,” she said.

And what she learned from Chang-Tien Lu about various algorithms in centrality and geospatial information systems “actually helped me get my current job,” she said.

Sistrunk’s research focuses on the intersection of computer science, public schools, and geographical information systems.

At DAC, she has been part of a team developing Redistrict, an online interactive platform that uses data analytics and machine learning to help parents and other stakeholders better understand school rezoning plans and their potential effect on the community; share their comments and concerns about proposed plans; propose changes to boundaries; and even create their own plans. The team has been working with the Loudoun County Public Schools, among others.

Sistrunk has collaborated on two papers, “REGAL: A regionalization framework for school boundaries,” published in the proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2019 and Geospatial Clustering for Balanced and Proximal Schools, published in Education Advancements of Artificial Intelligence (EAAI) in January 2020.

She is projected to graduate by 2022.

“It is tough raising children, working, and going to evening classes, but I am so very grateful to my professors allowing me this top notch education,” Sistrunk said. “My experience with the faculty and my colleagues at DAC give me strength to go the extra mile.”

That extra mile includes volunteering. During the 2019-2020 school year, Sistrunk volunteered for Girls in Cyberjitsu, and ran a club at Marshall Elementary School in Manassas for STEAM arduino circuits robocrafts.

 

 

 

 

 


DAC Student Spotlight: Nikhil Muralidhar

Nikhil Muralidhar, DAC and UrbComp Ph.D. student in the Department of Computer Science

Graphic is from Muralidhar’s paper on “PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly”

Choosing to pursue a Ph.D. in computer science at Virginia Tech was easy for Nikhil Muralidhar.

“Virginia Tech was my top choice for good reason,” Muralidhar said. “It is known for its quality research and interdepartmental collaborations, for encouraging students to work on real world interdisciplinary applications, and for pioneering programs like UrbComp.”

Also factoring in his decision was the opportunity to join the Discovery Analytics Center.

“I had been following DAC’s track record of high quality, practical research since I was a Virginia Tech undergraduate. I am happy to be part of a rare breed of research labs with both extensive industrial and academic collaborations. The facilities are state-of-the-art and the faculty are approachable, helpful, and use their experience to guide their students to become successful researchers,” said Muralidhar, who is advised by Naren Ramakrishnan.

He is also a research trainee in the National Science Foundation-sponsored multidisciplinary Urban Computing graduate certificate program, which is administered through DAC.

The focus of Muralidhar’s research is on applied machine learning.

Wide applicability and the potential to create widespread impact drew him to the burgeoning fields of data mining and pattern recognition. For example, he said, researchers have been effectively using data mining techniques to forecast influenza seasonal dynamics, while others have trained machine learning models to detect gun shots.

“There have also been applications of machine learning in medicine for the early detection of certain neural disorders, for design of patient-focused cancer treatment programs, and even to aid researchers in the discovery of new potentially life-saving drugs,” said Muralidhar.

In his work, he uses prior domain knowledge to help machine learning models learn more effectively, especially under data paucity or with noisy data.

“I have incorporated prior domain knowledge to multiple domains including computational fluid dynamics as part of a team which developed a physics guided machine learning model for predicting particle drag forces in multi-phase fluid flows,” Muralidhar said.

He said that computational fluid dynamics (CFD), and specifically multi-phase flows (i.e fluid particle systems), are an integral part of propulsion, automobile design, pharmaceuticals, food processing, and many environmental applications. However, because running CFD simulations at fine-grained scales is expensive, researchers generally run coarse grained simulations of systems of interest.

“Coarse grained simulations involve many approximations and abstractions of the underlying physics leading to a degradation of simulation accuracy,” Muralidhar said. “The goal in my research has been to incorporate machine learning models accompanied with the known physics governing a particular CFD process to improve the overall accuracy of the various facets of coarse grained CFD simulation.”

Muralidhar’s paper, “PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly” was published at the 2020 SIAM International Conference on Data Mining.

Included among his other research collaborations are “Detection of False Data Injection Attacks in Cyber-Physical Systems using Dynamic Invariants” at the 2019 IEEE International Conference on Machine Learning and Applications; “Multivariate Long-Term State Forecasting in Cyber-Physical Systems: A Sequence to Sequence Approach” at the 2019 IEEE International Conference on Big Data; “DyAt nets: dynamic attention networks for state forecasting in cyber-physical systems” at proceedings of the 2019 International Joint Conference on Artificial Intelligence; and “Detection of false data injection attacks in power systems using multiplex invariant networks and domain knowledge” at the 2019 IEEE International Conference On Machine Learning And Applications.

Muralidhar, who also holds a master’s degree from George Mason University, is projected to graduate in June 2021. He would like to pursue a career in academic research as part of a research lab or as a faculty member after receiving his Ph.D.


DAC Student Spotlight: Lei Zhang

Lei Zhang, DAC Ph.D. student in the Department of Computer Science

Graphic is from Zhang’s research on “Situation-Based Interpretable Learning for Personality Prediction in Social Media”

Lei Zhang was a master’s degree student in software engineering at Jinan University in China when his advisor told him about meeting Chang-Tien Lu from Virginia Tech and how he was doing research with algorithms on Twitter. While they were using different platforms — Zhang’s own work was on Weibo, the largest Chinese microblogging website — he was interested to hear  about Lu’s research.

When he decided to pursue a Ph.D., Zhang decided to apply to Virginia Tech’s Department of  Computer Science. As it turned out, Lu is now his advisor.

Zhang’s current research at the Discovery Analytics Center includes graph structure learning.

“Graph neural network models have shown that they can be widely used for urban computing, neuroscience, biology, and many other fields,” said Zhang. “However, the graph structure can be either non-existing or not optimal for specific objectives. Take a traffic network as an example. Graph structure learning techniques can construct a causal graph of traffic flow changes which is more informative than just the grid graph.”

Zhang has also integrated psychology and machine learning. He has designed a text classification model utilizing the DIAMONDS situation taxonomy from psychology. Zhang is also working on some neuroscience-inspired models such as spiking networks, oscillation networks, and echo state network.

“My research interests originated from my experience reading popular science books when I was younger,” said Zang. “Three topics I liked most are complexity science, psychology, and AI. While my own research did not start from the most relevant areas, it is getting closer and closer,” he said.

Zhang said working with “very smart” DAC students is an added plus to what he is learning from expert machine learning/data mining professors on campus.

Lu and another DAC student were among Zhang’s collaborators on a paper he presented at the 2018 IEEE International Conference on Big Data entitled “Situation-Based Interpretable Learning for Personality Prediction in Social Media.”

Another one of Zhang’s papers, “Acoustic differences between healthy and depressed people: a cross-situation study,” was published in the October 2019 issue of BMC Psychiatry.

He holds a bachelor’s degree in software engineering from Northeastern University, China, in addition to his master’s degree from Jinan University.  Projected to graduate in Spring 2021, Zang said his ideal position would be in an interdisciplinary research lab for AI and psychology/neuroscience.


DAC Student Spotlight: Mohammad Raihanul Islam

Mohammad Raihanul Islam, DAC Ph.D. student in the Department of Computer Science

Graphic is from Islam’s paper on “RumorSleuth: joint detection of rumor veracity and user stance”

Classifying rumors and fake news in social media is the focus of Mohammad Raihanul Islam’s work at the Discovery Analytics Center.

“A rumor generally refers to an interesting piece of information — widely disseminated through a social network — that is not easy to substantiate,” said Islam, a Ph.D. student in computer science.

Later, it can turn out to be true, false, or remain unverified.

“The threat of rumors and fake news is very real and identification is crucial because rumors and fake news can lead to deleterious effects on users and society,” he said. “For example, spreading unverified malicious content could cause severe economic downfalls within a short period of time.”

The objective of his research, he said, is to develop a range of machine learning methods to effectively detect and characterize rumor veracity in social media.

DAC’s emphasis on applied machine learning, especially in social network analysis, is what attracted Islam to the center. Advised by Naren Ramakrishnan, he is on track to graduate this spring.

In the first part of his Ph.D. thesis, Islam worked on creating rich representation for users that can be helpful in rumor classification. He then applied this representation to classify which conversation is talking about a fake news/rumor.

Now, in the final stages of his research, Islam is focusing on creating a generative model for rumor classification using state-of-the-art deep learning models.

“At DAC I have enjoyed working on applied machine learning problems of my choosing and the opportunity of collaborating with fellow graduate students,” Islam said.

He is first author on four major publications: Inferring Multi-Dimensional Ideal Points for US Supreme Court Justices, 2016 Association for the Advancement of Artificial Intelligence (AAAI) conference; DeepDiffuse: Predicting the ‘Who’ and ‘When’ in Cascades, 2018 IEEE International Conference on Data Mining (ICDM); RumorSleuth: joint detection of rumor veracity and user stance, 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM); and NActSeer: Predicting User Actions in Social Network using Graph Augmented Neural Network, 2019 ACM International Conference on Information and Knowledge Management (CIKM).

Islam personally presented the first two papers; the other two were presented by collaborators.


DAC Student Spotlight: Debanjan Datta

Debanjan Datta, DAC Ph.D. student in the Department of Computer Science

Graphic is from Datta’s paper on ”Detecting Suspicious Timber Trades”

Debanjan Datta’s interest in data mining focuses on systems that perform anomaly detection with both interpretability and the ability to incorporate domain knowledge and human input.

In a recent Discovery Analytics Center study with the World Wildlife Fund, Datta developed a framework that can apply machine learning on massive trade datasets to detect patterns of suspicious timber records that relate to possible illegal trade. He shared results of the study, “Detecting Suspicious Timber Trades,” at the Conference on Innovative Applications of Artificial Intelligence (IAAI) earlier this month.

The research involves analyzing, record-by-record, thousands of lines of export and import data.

“By analyzing available timber data, along with open source domain knowledge, we are trying to develop software and algorithms that will help flag suspicious timber at the border in real time. Such a human-machine approach can improve both efficiency and effectiveness,” Datta said.

Datta, a Ph.D. student majoring in computer science, is advised by Naren Ramakrishnan.

“Projects aimed at solving real world challenges with state of the art approaches that are not restricted to one area really piqued my interest in joining DAC,” said Datta. “DAC’s approach to research offers a breadth that is difficult to find.”

Flexibility to explore research areas and opportunities to collaborate on interesting projects and learn from people having a myriad of interests are major positive aspects of being a DAC student, he said.

Datta earned a bachelor of engineering in computer science and engineering from Jadavpur University, India, and was a software development engineer at Yahoo, Inc. both in  Bangalore, India, and in Sunnyvale, California, prior to pursuing his Ph.D.

He is projected to graduate in Fall 2021. After graduation, Datta said he “would like to continue in research, probably in a lab that carries on work with practical applications.”


DAC Student Spotlight: Sneha Mehta

Sneha Mehta, DAC Ph.D. student in the Department of Computer Science

Graphic is from Mehta’s paper on “Event Detection using Hierarchical Multi-Aspect Attention”

Sneha Mehta, a Ph.D. student in computer science at the Discovery Analytics Center, was in New York City this week to present “Simplify-then-Translate: Automatic Preprocessing for Black-Box Translation” in a talk and poster presentation at the main AAAI Conference on Artificial Intelligence.

The paper represents her work on novel methods to improve machine translation for subtitles while an intern at Netflix for two consecutive summers.

In fact, last summer was a busy one for Mehta, who is advised by Naren Ramakrishnan. In addition to an internship at Neflix headquarters in Los Gatos, California, she was also selected to attend the Deep Learning and Reinforcement Learning Summer School (DLRLSS), in Edmonton, Alberta, Canada.

“I applied to the Summer School because both deep learning and reinforcement learning are very relevant to my work both at DAC and the work I was doing at Netflix,” Mehta said. “Hearing directly from some of the pioneers in the field was a great – and invaluable – experience.”

As an undergraduate, Mehta did a couple of internships where she was introduced to data mining and deep learning. “As a result, I decided I wanted to study these areas in more depth,” Mehta said.

Mehta earned a bachelor’s degree in computer science and a master’s degree in mathematics from BITS Pilani University in India.

“I was attracted to Virginia Tech and DAC to pursue a Ph.D. because of the faculty who are doing cutting edge research in the fields of data science and machine learning,” Mehta said.  “What I like best about being a DAC student is the opportunity it provides for interdisciplinary collaborations.”

Last May, Mehta presented a poster of her collaborative work at DAC, “Event Detection using Hierarchical Multi-Aspect Attention,” at The Web Conference.

Recently, Mehta was named a 2020 Twitch Research Fellow, one of five students across the country to receive the $10,000 award to support her academic research and an offer to connect with a team at Twitch for a full-time paid internship at Twitch HQ.

Mehta has presented a successful preliminary of her dissertation on new frameworks for event detection and extraction and is projected to receive her Ph.D. in Fall 2020. After graduation, she would like to have a research role in industry.


DAC Student Spotlight: Abdulaziz Alhamadani

Abdulaziz Alhamadani, DAC Ph.D. student in the Department of Computer Science

Graphic is from Alhamadani’s paper “Batman or the Joker? The Powerful Urban Computing and its Ethics Issues”

Abdulaziz Alhamadani’s path to computer science is somewhat atypical.

Having already earned a bachelor of arts degree in English language from Umm Al-Qura University and a master of arts in English literature from King AbdulAziz University, Alhamadani made a decision to combine his knowledge of linguistics with computer science. That resolve led him to the University of New Hampshire, where he earned a master of science degree in computer science.

Now, as a Ph.D. student in computer science at the Discovery Analytics Center,  Alhamadani is focusing on Arabic natural language processing, especially text summarization and text classification. Advised by Chang-Tien Lu, his work involves automatic archiving of news without human annotation and summarizing daily news articles to headlines.

“Being a DAC student offers an eclectic array of trending areas of research ranging from data analytics to natural language processing,” said Alhamadani. “I like the combination of research areas and how everyone — my advisor Dr. Lu, other distinguished professors at the center, and fellow students — motivate me. They are always willing to share their views and offer their help.”

“Collect Ethically: Reduce Bias in Twitter Datasets,” his collaborative work with Lu and two other students, was presented at SIMBig2019 last summer. In the study, the research team addresses factors that lead to sampling bias, presents case studies it encountered, proposes an approach that will reduce sampling bias and flaws in datasets collected from Twitter, and then follows the proposed guidelines to conduct two case studies to achieve a larger dataset.

“The results show that using multiple Twitter application programming interfaces for data collection is the best way to obtain a randomly sampled dataset,” Alhamadani said.

Another study, “Batman or the Joker? The Powerful Urban Computing and its Ethics Issues,” was published in December by ACM SIGSPACIAL.

Alhamadani said he is currently working to create the largest database for Arabic news articles for text summarization.

He serves as vice president of the Graduate Student Assembly in northern Virginia and has helped organize graduate student activities. In December he participated in the annual Scottish Walk Parade in Old Town Alexandria, where President Sands was Grand Marshal.

Projected to graduate in 2022, Alhamadani’s career goal is to be a professor at a Saudi Arabia university.

 

 

 


DAC Student Spotlight: Jinwoo Choi

Graphic is from Choi’s paper on “Why Can’t I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition”

Jinwoo Choi, DAC Ph.D. student in the department of Electrical and Computer Engineering

 

 

 

 

 

 

 

 

 

 

Jinwoo Choi will be heading to Snowmass Village, Colorado, in March to present “Unsupervised and Semi-Supervised Domain Adaptation for Action Recognition from Drones” during the 2020 Winter Conference on Applications of Computer Vision. WACV is a premier meeting of the IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence.

Choi, a Ph.D. student in the Bradley Department of Electrical and Computer Engineering, will give both a short oral presentation and a poster presentation on the paper.

Last month, he was in Vancouver, Canada, to present a poster, “Why Can’t I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition,” at NeurIPS 2019.

Among Choi’s collaborators at the Discovery Analytics Center is his advisor, Jia-Bin Huang.

“I was attracted to Virginia Tech and DAC by the students and faculty,” Choi said. “Being surrounded by people doing good research in data science gives me inspiration and keeps me motivated.”

Choi said his interest in computer vision and machine learning lies more specifically in making machines to understand what is going on in a video.

“Video understanding is a relatively under-explored area in the computer vision community,” he said, “but it can be crucial. For example, autonomous driving vehicles need to tell what pedestrians around the vehicles are doing in order to prevent accidents.”

Choi’s internship last summer was in the Media Analytics Department at NEC Labs America in San Jose, California, where he conducted research on unsupervised domain adaptation for videos. He has submitted work from this internship experience for consideration to an upcoming conference.

His projected graduation date is December 2020.

Choi earned a bachelor’s degree in electrical engineering and a master’s degree in electrical engineering and computer science from Seoul National University, Korea.

Eventually, he said, he would like to return to South Korea as a professor to continue his own research on computer vision/machine learning and teach students how to do research.


Congratulations to DAC summer and fall 2019 graduates!

Chris North (left), associate director of DAC and professor of computer science, with John Wenskovitch (right), DAC Ph.D. graduate at the Fall 2019 commencement ceremony

Virginia Tech’s Fall Commencement ceremony was held on Friday, Dec. 20.

New summer/fall alumni include four Ph.D. students and one master’s student at the Discovery Analytics Center.

“We are very proud of our graduates and the impactful research they have undertaken at DAC while pursuing their graduate degrees,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the center. “We wish them continued success as they embark on their academic and industry careers.”

Following are the DAC graduates: 

Shuangfei Fan, advised by Bert Huang, received a Ph.D. in computer science. Her research interests are machine learning, graph analysis and deep learning, and her dissertation title is “Deep Representation Learning on Labeled Graphs.” Fan joins Facebook as a research scientist. In that position she will work to apply machine learning techniques to help people build community and bring the world closer together.

 Alyssa Herbst, advised by Bert Huang, received a master’s degree in computer science. Her research interests are active learning and machine learning, and her thesis title is “Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques.” Herbst joins Facebook as a software engineer.

Yaser Keneshloo, co-advised by Naren Ramakrishnan and Chandan Reddy, earned a Ph.D. in computer science. His research focused on tools to support news agencies with a dissertation titled “Addressing Challenges of Modern News Agencies via Predictiv Modeling, Deep Learning, and Transfer Learning.” Keneshloo is senior manager for the Advanced Data Science Team at Marriott.

Matthew Slifko, advised by Scotland Leman, received a Ph.D. in statistics. He was also a National Science Foundation Research Trainee in the UrbComp certificate program. His dissertation title is “The Cauchy-Net Mixture Model for Clustering with Anomalous Data.” His research focused on the development of a framework for clustering and predictive modeling in the presence of anomalous data, with an application toward predicting housing prices. Currently, Slifko is assistant professor of statistics in the Department of Mathematical Sciences at High Point University.

John Wenskovitch, advised by Chris North, earned a Ph.D. in computer science. His dissertation title is “Dimension Reduction and Clustering for Interactive Visual Analytics.” Currently a visiting assistant professor in the Virginia Tech Department of Computer Science, Wenskovitch’s work focuses on the interconnecting roles of visualization and machine learning in visual analytics systems, exploring techniques to enable systems to infer the interests and intentions of the interacting user, thereby adapting and personalizing the visualization and underlying models. 

 


DAC Student Spotlight: Anika Tabassum

Anika Tabassum, DAC and UrbComp student in the Department of Computer Science

Graphic is from Tabassum’s paper on “Urban-Net: A System to Understand and Analyze Critical Emergency Management”

 

 

 

 

 

 

 

Urban computing plays a large part in Anika Tabassum’s research at the Discovery Analytics Center as she attempts to answer questions related to critical infrastructure systems: Which power grids/substations are most vulnerable and need immediate action to recover during a hurricane? Which regions are highly affected during a power outage? Are there patterns or similarities in power outages among the connected components?

Tabassum uses optimization and learning-based algorithms when trying to solve energy challenges like these. A Ph.D. student in computer science, she is also a research trainee in the National Science Foundation-sponsored UrbComp graduate certificate program, which is administered through DAC.

Critical infrastructure systems such as power, transportation, communication, and healthcare are crucial for sustaining day-to-day commodity flows vital to national security, economic stability, and public safety, said Tabassum. Failure of even a small part of such systems — caused by any natural or human-made disaster — can trigger widespread cascading failures impacting many other interdependent modules and disrupt the functionality of the entire system.

“It is challenging to understand and analyze large scale data gathered from these systems in terms of graph networks and time-series sensor technologies since they are unstructured and highly dynamic,” said Tabassum. “But extracting information like anomalies, similar patterns, and actionable insights from critical infrastructure systems can help domain experts assess, in a comprehensive manner, the complex interdependencies and failure dynamics over these systems and can also facilitate faster and less expensive decision-making,” said Tabassum.

Last summer Tabassum was a research intern at the Oak Ridge National Laboratory in Oak Ridge, Tennessee, where she applied her data mining and visualization skills in a U.S. Department of Energy (DOE) project on Smart Neighborhood. This study was accepted at the ACM International Workshop On Urban Building Energy Sensing in New York last month.

Previously, she collaborated with the Oak Ridge National Laboratory, DAC alumnus Liangzhe Chen, and her advisor B. Aditya Prakash on ‘“Urban-Net: A System to Understand and Analyze Critical emergency management.” Tabassum presented this paper in the Project Showcase at ACM SIGKDD’19, in Anchorage, Alaska, in August.

Another of their papers, “Data Mining Critical Infrastucture Systems: Models and Tools,” was published in the December 2018 issue of the IEEE Intelligent Informatics Bulletin.

With a bachelor’s degree in computer science and engineering from the Bangladesh University of Engineering and Technology, Tabassum was attracted to DAC because of the potential and interesting research in data mining and applied machine learning it offered.

“I found my advisor’s work exceptionally intriguing and very much suited to my research interest,” she said. “And once I joined DAC I found a strong collaboration of research and extremely friendly and cooperative graduate students.”

When Prakash relocated to DAC’s Arlington location in the fall, Tabassum also moved from Blacksburg.

She is on track to graduate in December 2021 and is aiming for a position as an industry researcher or an academic post-doctoral researcher.


Focus on Wei Wang…..a DAC alumnus interview

Wei Wang, DAC alumnus

Wei Wang graduated with a Ph.D. in computer science in 2017 and joined the Language and Information Technology (LIT) group at Microsoft Research, Redmond, Washington, as an applied scientist. Recently, he was promoted to senior applied scientist.

Did transitioning from academia to industry hold any real surprises for you?

For the most part, problems that we try to solve as Ph.D. students are well-defined and have benchmarks. We just need to propose novel approaches to push the-state-of-the-art. The problems I face now often require much more effort to build an end-to-end solution.

What are your responsibilities at Microsoft Research?

I mainly work in the area of natural language understanding and user behavior modeling. I also collaborate with the product team to transfer state-of-the-art technique to the product.

Do you use what you learned at DAC?

While the approach to problems might be somewhat different, my current lab environment is not that much different from an academic research lab. So what I learned at DAC is useful in my job —  things like how to form the right research questions, how to plan and execute a project to meet a deadline, and how to write a paper.

About the time you moved from Blacksburg to Redmond there was another big change in your life, right?

Yes, two and a half years ago, my wife Ying and I welcomed our first child, Aaron. We now have a second son, Aiden, who is nine months old. Ying and I met while we were both students. She earned a master’s degree in computer science from Virginia Tech.

You earned a bachelor’s degree in applied science from Shanghai University in 2007.  How did you wind up at DAC?

After graduating I worked in Shanghai City as a software engineer for four years. From this experience, I saw the great potential for applying data mining in business and that motivated me to learn more about data mining. I decided to apply for a master’s program in the United States and while I had several choices, I decided on Virginia Tech and, because of my research area, DAC was a natural choice for me.

Did you have a mentor at DAC?

I joined DAC as a master’s student and transferred to the Ph.D. program after working with my advisor, Dr. Naren Ramakrishnan, for two years. He definitely had the largest impact on me. He provided me with tremendous freedom and showed great patience as I explored different research ideas. At the same time, he made sure I was always on the right track.

 Are internships important?

I would advise current DAC students to intern in at least one industry research lab as it helps you get a sense of the difference between research in academia and industry and choose the career path that suits you best.

Any other advice for current Ph.D. students?

Be self-motived and proactive. Take advantage of opportunities to talk to students and professors from different fields because many good ideas come out of cross-field collaboration.

 

 


DAC Student Spotlight: Sirui Yao

Sirui Yao, DAC Ph.D. student in the Department of Computer Science

Graphic is from Yao’s NeurIPS 2017 paper “Beyond Parity: Fairness Objectives for Collaborative Filtering”

Sirui Yao studies the biases of recommender systems.

“A recommender will often suggest different courses to male and female college students because based on historical data, there are differences in course preference between these two groups,” said Yao, a Ph.D. student in computer science at the Discovery Analytics Center.

“Over-leveraging this gender-based pattern encourages stereotypes and creates an even bigger —and undesirable — gap between demographic groups, especially in areas actively encouraging equality, such as engineering,” she said.

Yao’s work proposes methods for measuring, analyzing, and mitigating unfairness in recommender systems. She was awarded a 2018-2019 Deloitte Foundation Data Analytics Fellowship in the amount of $10,000 to fund her research.

Her advisor, Bert Huang, introduced Yao to this topic three years ago. “I realized this is a very critical yet fairly unexplored area in machine learning, so I wanted to focus on it and make whatever contributions I can to fill this vacuum,” she said.

“Being a DAC student means I am always informed about exciting data science projects and surrounded by people who have a lot of expertise, passion, and creativity in data analytics,” Yao said. “Such an environment encourages me to learn more and do more.”

Yao works in Huang’s Machine Learning Laboratory and the two have collaborated on research, including “On the Need for Fairness in Financial Recommendation Engines,” which Yao shared at the NeurIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy.

She presented “Beyond Parity: Fairness Objectives for Collaborative Filtering” at the main NeurIPS 2017 conference and  “New Fairness Metrics for Recommendation that Embrace Differences” at the KDD 2017 Workshop on Fairness, Accountability, and Transparency.

This past summer, Yao interned at Google Brain in New York City, where she worked on a research project that designs a trajectory simulation and analysis framework for studying the long-term dynamics of recommender systems. This research has been submitted to WWW 2020: The Web Conference.

Yao earned a bachelor of science degree in computer science and technology from the Harbin Institute of Technology in China and is projected to graduate from Virginia Tech in December 2020.


DAC Student Spotlight: Taoran Ji

Taoran Ji, DAC Ph.D. student in the Department of Computer Science

Graphic is from Ji’s paper on “Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks”

Interested in data mining and machine learning, Taoran Ji, a Ph.D. student in computer science, said he was drawn to the Discovery Analytics Center because it plays an active role in these fields.

“There are so many projects at the center that provide great opportunities to practice these techniques in real world applications,” Ji said.

Ji, advised by Chang-Tien Lu, has focused his research on a range of topics, all of which he has been able to explore by collaborating with Lu and other faculty and students at DAC. These include event detection/prediction and associated applications such as civil unrest detection, airport threat detection, transit disruption detection, and emerging science and technology prediction.

Among his published papers, two were included in proceedings at conferences held this year.

“Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks” forecasts the popularity and value of a technology of interest by developing a deep learning model to predict the number of citations that will be received by a patent or paper of interest, which can be used as an indicator of emerging technologies. Ji presented this paper at the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019) in Macao, China, in August.

This research reflects Ji’s  interest in discovering and forecasting emerging technologies which, he said,  have great potential in the research field and can bring value to the market.

“We were able to attain access to a U.S. patent dataset, which can be viewed as the direct scientific output of science and technology activity in the industry,” said Ji. “Inspired by previous works in patent-based technology, we saw the potential value of forecasting a patent’s future citations.”

The second paper, “Feature Driven Learning Framework for Cybersecurity Event Detection,” leverages the huge volume of social media data to focus on using data mining and machine learning techniques to detect ongoing cybersecurity events and develop algorithms to automatically identify and collect online discussion and online complaints of abnormal status such as slow internet service and suspicious email logins. It was published in the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019).

Ji’s work also includes event detection using data from social media like Twitter.

“My team and I have developed a data mining method to identify tweets denoting abnormal or suspicious events — someone brings a knife to the airport, for example — which can potentially cause security problems,” said Ji.  “And in another study, we were able to detect metro service disruptions by analyzing tweets posted from the Washington, D.C., area.”

Some of this work is found in his other published papers: “Multi-Task Learning for Transit Service Disruption Detection”  (ASONAM 2018); “Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media” (CIKM 2017); and “Determining Relative Airport Threats from News and Social Media” (AAAI 2017).

Ji received his master’s degree in computer science from Xidian University, China. His projected Ph.D. graduation date is 2020.


Srijan Sengupta awarded NIH grant to study unstructured data that can improve patient safety

Srijan Sengupta, DAC faculty member in the department of statistics

Reports that medical errors are the third leading cause of death in the United States have led the Institute of Medicine and several state legislatures to suggest that data from patient safety event reporting systems could help health care providers better understand safety hazards and, ultimately, improve patient care.

“Tens of thousands of these safety report databases provide a free text field that does not constrain the reporter to fixed, predefined categories,” said Srijan Sengupta, assistant professor of statistics in the College of Science and a faculty member at the Discovery Analytics Center.

Sengupta has received an $815,218 Research Project Grant (R01) from the National Institutes of Health to develop novel statistical methods to analyze such unstructured data in safety reports. Click here to read more about Senputa’s grant.


DAC Student Spotlight: Shuangfei Fan

Shuangfei Fan, DAC Ph.D. student in the Department of Computer Science

Graphic is from Fan’s research on “Deep Generative Models for Generating Labeled Graphs”

In disease control and prevention, understanding how an emerging infectious disease can spread beyond the visible network is important.

Marketers posting ads on an online social network can benefit from knowing how their information will spread beyond ego networks.

These two scenarios provide good examples for practical application of Shuangfei Fan’s research using deep representation learning algorithms on labeled graphs to model graph generation and graph evolution.

“A model of graph evolution would be a powerful tool for both predicting the future and the transformation of networks,” said Fan, a Discovery Analytics Center Ph.D. student in computer science.

Fan said that graphs are complex and versatile data structures that can be used to represent various kinds of real-world data with complex relationships. However, some special properties of graphs — such as discrete form and order-invariance — make generation of graphs a harder problem than it might be for other data types such as images and natural language.

“So this is a challenging and interesting area to explore,” said Fan, who earned a bachelor’s degree in computer science and technology from the University of Electronic Science and Technology of China.

At a workshop on Deep Generative Models for Highly Structured Data at the 2019 International Conference on Learning Representations, Fan presented the work she collaborated on with her advisor Bert Huang, “Deep Generative Models for Generating Labeled Graphs.”

Her other work with Huang includes “Recurrent Collective Classification. Knowledge and Information System,” in 2018, and “Training Iterative Collective Classifiers with Back-Propagation,” presented at the 12th Workshop on Mining and Learning with Graphs in August 2016.

Fan met Huang after she had already began her Ph.D. program at Virginia Tech. “I asked to join his Machine Learning Laboratory because we had the same research interests and luckily he said ‘yes,’” Fan said.

“At the Discovery Analytics Center I have had the opportunity to work with many talented and great researchers and to access computing resources that are valuable to my work,” she said.

Fan will be graduating at the end of the year and is planning on an industry career.

 


DAC Student Spotlight: Fanglan Chen

Fanglan Chen, UrbComp and DAC Ph.D. student in the Department of Computer Science

Graphic is from Chen’s research on “Mitigating Uncertainty in Document Classification”

Motivated to improve the health and quality of urban environments through new data sources and methods, Fanglan Chen is simultaneously pursuing a Ph.D. in computer science and a master’s degree in urban planning.

Additionally, she plans to earn a graduate certificate in Data Analytics, offered through the Discovery Analytics Center. She already holds a graduate certificate from the multidisciplinary National Science Foundation-funded UrbComp program, which is also administered through DAC.

Chen’s research explores how new data sources and methods can be usefully applied to persistent urban issues. Her advisor is Chang-Tien Lu.

“To conduct a rationale data-drive urban study it is important to have both domain knowledge and data analytics skills,” Chen said. “Domain knowledge is fundamental in guiding why we decide to approach the urban challenge in a particular way and how a project of this scope would impact the citizens it is designed to help. Data analytics underpins modern urban planning decisions. Knowing which questions to ask and how they might be approximated by the data at hand are of equal importance in urban studies.”

Chen’s interests lie in spatial data mining, urban computing, and graph neural networks. She wants to design a novel Graph Convolutional Neural Network (GCN) model with data-driven graph filters to deal with urban computing problems.

“Graphs offer powerful representation of real-world datasets in various domains, such as networks, social links, molecular structures, and unstructured data as images and text,” Chen said. “In exploring graph neural networks, besides the model performance, my interest is in why and how it works because the urban computing problems I work on require the interpretability of the method for better decision making.”

One real-world example, she said, is forecasting hourly demand at station-level in large bikeshare networks via learning hidden pairwise correlations between stations.

Chen earned a bachelor’s degree in architecture from Wuhan University of Technology, China.

High-quality programs that allowed her to tailor curriculum to her research interests and to collaborate with faculty attracted her to Virginia Tech and DAC. She joined the northern Virginia campus because she believed it would give her exposure “to a great variety of learning and networking opportunities with nearby companies and agencies. Besides, I personally enjoy urban life,” she said.

The UrbComp program, she said, also offered a number of advantages. “It is wonderful platform to discuss current data analytics approaches in different fields and bridge the gap between the academic and industrial worlds,” said Chen, who is open to a career in either one after her graduation, projected for 2022.

Chen has collaborated on two papers included in conference proceedings: “REGAL: A Regionalization framework for school boundaries,” ACMSPATIAL 19; and “Mitigating Uncertainty in Document Classification,” (Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL2019).

She was also one of the presenters of  “A First Look: Comparison of Users and Usage Patterns of Dockless and Docking-Station-Based Bikeshare Systems in Washington, DC,” during a poster session at the 2019 Transportation Research Board Annual Meeting.


DAC Student Spotlight: Alexander Rodriguez

Alexander Rodriguez, DAC Ph.D. student in the Department of Computer Science

Alexander Rodriguez was a master’s degree student in data science at the University of Oklahoma when he met B. Aditya Prakash at the 2017 ACM SIGKDD Conference on Knowledge Discovery and Data Mining. That meeting sealed his decision to apply to the Ph.D. computer science program at Virginia Tech.

“I was excited about the problems he was working on and I felt he was a knowledgeable person from whom I could learn how to become a researcher,” said Rodriguez of his current advisor.

The opportunity to be part of the Discovery Analytics Center also played a part in his decision to come to Virginia Tech.

“DAC researchers are leaders in demonstrating the capabilities of data mining to drive common good,” Rodriguez said. “It is an exciting time to work on this as current sensing technologies allow us to collect large amounts of data about dynamical processes in complex systems, including opinion formation in social networks, uncertainty propagation in economic networks, and human mobility in cities. Understanding these complex phenomena embedded in big data can ultimately assist city planners, government officials, engineers, and others to make better decisions for their communities.”

Rodriguez’ first collaborative project with Prakash involves inferring missing data from a carefully collected sample. This work, motivated by urban computing, is currently in submission.

With another DAC Ph.D. student, Bijaya Adhikari, Rodriguez is co-leading Virginia Tech’s team (which includes Prakash and Naren Ramakrishnan) in the FluSight challenge, an epidemic prediction initiative at the Centers for Disease Control and Prevention (CDC) where researchers from all over the United States test their best methods for forecasting seasonal influenza.

Rodriguez spent last summer as a research intern at WalmartLabs in Sunnyvale, California. Assigned to the Machine Learning Ranking team, he also collaborated with other teams to propose a representation learning approach to help understand relationships between user queries, work that is continuing and will be submitted to a future conference.

Rodriguez earned his undergraduate degree in mechatronics engineering at the National University of Engineering, Peru, where he took courses in artificial intelligence for the first time. While there, he won a scholarship that allowed him to spend a year at the University of Oklahoma as an exchange student, where he was involved in a research project in applied machine learning.

After graduation, Rodriguez would like to pursue a career as a university professor or as an industry researcher.


DAC Student Spotlight: Alyssa Herbst

Alyssa Herbst, DAC M.S. student in the Department of Computer Science

Graphic is from Herbst’s research on “Active Learning by Greedy Split and Label Exploration”

After receiving a master’s degree in computer science at Fall Commencement, Alyssa Herbst will head to New York City. She has already accepted a position as software engineer at Instagram, where she interned on the Shopping Machine Learning team this past summer.

Herbst’s interest in machine learning sparked when, as an undergrad in the Department of Computer Science, she took a class taught by Bert Huang. She wound up working in Huang’s Machine Learning Laboratory on a twitter scraping project to assist with cyberbullying research.

“As part of this research, we had a corpus of tweets that we wanted to label as either ‘bullying’ or ‘not bullying,’ but a limited crowdsourcing budget. So we started to think about what it would look like to ‘guess’ the labels of tweets with some degree of certainty if crowdsource workers labeled some of the tweets,” said Herbst.

“I came into the lab with little knowledge about machine learning and ended up learning so much from attending meetings and going over research papers,” said Herbst. “I was happy to be able to develop my own research as a grad student with Professor Huang.” Huang is now her advisor at the Discovery Analytics Center.

Herbst said she really appreciates the opportunities she has had at DAC  “to collaborate with really smart, talented people and to gain exposure to other areas of research.”

Her current work, a form of active learning, or human-in-the-loop machine learning, is an extension of the twitter scraping project she worked on as an undergrad.

“We aim to guess the labels of a large unlabeled dataset in a low-budget setting by having a person iteratively label data from the dataset and split apart the data into groups when we see that label groupings have emerged,” she said.

“Data with similar features to a label grouping will likely have the same label. We make decisions on when to split data into groups and when to label data by using bounds that tell us how confident we are about the labels we infer,” said Herbst.

She and Huang have collaborated on “Active Learning by Greedy Split and Label Exploration.”  And last December, she presented “Interactive Learning by Uniformity Propagation Strategy” at the Women in Machine Learning poster session at the Conference on Neural Information Processing Systems (NeurIPS 2018).


Focus on Mahmud Shahriar Hossain…..a DAC alumnus interview

Mahmud Shahriar Hossain, DAC Alumnus

Mahmud Shahriar Hossain was recently promoted to associate professor of computer science, with tenure, at the University of Texas at El Paso (UTEP). He leads the university’s Discovery Analytics Lab. Hossain earned his Ph.D. in computer science from Virginia Tech in 2012 and joined UTEP as an assistant professor in 2013.

While at the Discovery Analytics Center at Virginia Tech, his work with advisor, Naren  Ramakrishnan focused on event analysis, “storytelling,” and data abstraction techniques like alternative clustering and scatter/gather clustering. He applied his methods to solve a broad spectrum of problems in multiple disciplines, including national security, biomedical science, and mechanical engineering.

Still fascinated by event analysis and prediction using news and also using scientific literature, Hossain received a three-year award from the U.S. Army Engineering Research and Development Center, U.S. Army Corps of Engineers, to conduct research related to this topic. His team, which included two Ph.D. students, developed advanced language models that incorporated text, images, and temporal aspects.

In a recent interview, Hossain reflected on his life as a DAC student, shared his passion for teaching, and offered advice garnered from his own experience.

— When did you know that academia was the right path for you?

From childhood, I knew I wanted to be a teacher. But, like many kids in Bangladesh, I also wanted to be an engineer and a doctor – all at the same time! Slowly, I figured that academia is the only place where I can be all three. I have a doctorate in computer science, I teach, and I am an engineer. So academia is like a dream come true for me.

— How did you wind up at Virginia Tech and more specifically, at DAC?

My wife Monika Akbar and I were pursuing our master’s degrees at Montana State University while applying for admission to Ph.D. programs. We had a number of options. But the program director at Virginia Tech, Dr. Ramakrishnan, was the only one who asked to speak with us by phone. I knew of him because he had chaired the IEEE International Conference on Data Mining the year before. I was fascinated by his research, which perfectly aligned with my own interest in data mining and machine learning. After a 35-minute conversation, Monika and I agreed Virginia Tech was our best option and I joined DAC right from the start.

— Once you were actually settled in, what did you like best about DAC?

The whole time I was working on my Ph.D., DAC’s laboratory environment, with opportunities to collaborate, had the most impact on me. It helped me grow as a researcher. The lab was really our first home. Our apartment was second.

— How important do you think mentoring is for graduate students?

As a first-year student, I met Dr. Ramakrishnan three to four times a week for his feedback on my research. It was so encouraging and valuable to have that kind of experience. Now, on the faculty side, I view mentoring as a critical element of my job. There are so many aspects — from research to course work to work-life balance to having a plan for the future. I take it all very seriously. As mentors, we need to make sure that our graduate students are utilizing their time and energy well for their professional development.

— Are you using what you learned at DAC in your current position?

Oh, yes. Everything at DAC was state-of-the-art. And I am guided by what might be the biggest lesson I learned: Never become too satisfied with your research.

— Reflecting on your own experience, can you offer any advice

   …for current DAC students?

Develop as many skills as you can. This includes writing, presentation, other communication and time-management skills.

Collaborate with researchers from other labs. Being able to work with diverse disciplines is a strength that you can leverage to grow your research. Additionally, cross-disciplinary research is beneficial for society.

If your goal is academia, learn how to write a proposal.

   …for new graduates entering the workforce?

Be willing to go beyond your comfort zone.

   …for professors seeking tenure?

Collaboration is the key.

— Aside from your research, do you have a special interest?

Yes. It stems from my passion for teaching. I believe that anyone can succeed in learning computer skills if they have the appropriate, easy-to-understand materials. Monika and I designed a website, Computing for All. We develop programming-related videos and upload them to the site. We hope that learners worldwide will benefit. We launched about five months ago and have about 500 subscribers.

— Do you have any leisure time left for hobbies?

Not many people know this about me, but I love woodworking.

 

 

 

 


DAC Student Spotlight: Shane Bookhultz

Shane Bookhultz, DAC and UrbComp Ph.D. student in the Department of Statistics

Graphic is from Bookhultz’s poster presentation “Measuring Polarity from News Sources: a Topic Modeling Approach”

“Working with text data is challenging but that is what I like about it,” said Shane Bookhultz, a Ph.D. student in the Department of Statistics. “It is inherently noisy because of different mannerisms, choices of words, and tones. Unlike numerical data, which is pretty concrete, text data can have various interpretations.”

Bookhultz — who is also a research trainee in the National Science Foundation-sponsored Urban Computing graduate certificate program — was introduced to this research area by his advisor, Scotland Leman. Then he started exploring more text issues with different media.

Using media like news, twitter, etc., he tracks information flows across time and uses these flows to generate a polarization “barometer” to indicate which days are more inciting than others.

“If we can detect a polarizing and potentially high impact event in advance we may be able to help diffuse it,” he said.

Bookhultz has presented a poster of his work, “Measuring Polarity from News Sources: a Topic Modeling Approach” at two conferences: in May at the 2019 Spring Research Conference of the Institute of Mathematical Statistics and the American Statistical Association Section on Physical and Engineering Sciences (SPES), which was hosted by the Department of Statistics at Virginia Tech, and at the American Statistical Association’s JSM2019 in Denver this past summer.

Bookhultz earned a bachelor’s degree in mathematics from Millersville University and a master’s degree in statistics from Virginia Tech.

“The positive environment of DAC, my department, and really the whole university is a big plus,” said Bookhultz. “And my colleagues are so friendly, all encouraging each other to do well.”

He said that DAC has also provided him with opportunities to attend interesting applied lectures and talks, participate in workshops, and gain access to unique data through corporate partnerships.

After graduation, projected for May 2020, Bookhultz said he would like to be in a position to help people understand statistics. “Whether that be as a college professor or by collaborating with industry experts, I am keeping my options open,” he said.


DAC Student Spotlight: Jianfeng He

Jianfeng He, DAC Ph.D. student in computer science

Along his educational path, Jianfeng He learned an important lesson: Having a good advisor should be the number one priority in choosing a Ph.D. program. The opportunity to work with Chang-Tien Lu drew him to Virginia Tech and the Discovery Analytics Center after spending a short period of time at another university.

He’s focus is on data analysis of social media and he is currently working on image editing based on user requests and text classification based on machine learning.

This research builds upon an interest that began while He was an undergraduate majoring in digital media technology at the Central China Normal University and required to learn media design software, including Adobe Photoshop, Adobe Premiere, and MAYA.

“As an undergraduate I was dealing with the media manually, now I want to understand it through AI,” said He.

As a master’s degree student in computer science at the University of Chinese Academy of Sciences, He researched cross-modal retrieval and had several papers published on this topic, including “Multi-label double-layer learning for cross-modal retrieval” (Neurocomputing, January 2018); “Adaptively Unified Semi-supervised Learning for Cross-Modal Retrieval” (proceedings of the 2017 International Joint Conference on Artificial Intelligence) “Efficient Cross-Modal Retrieval Using Social Tag Information Towards Mobile Applications” (2017 International Workshop on Mobility Analytics for Spatio-temporal and Social Data); and “Cross-modal Retrieval by Real Label Partial Least Squares” (proceedings of the 2016 ACM international conference on Multimedia, October 2016).

He came to the United States about a year ago and joined DAC in the Spring 2019 semester.

“What I like most about being at DAC,” said He, “is the friendly student-faculty relationship and student-student relationship.”

He said he has received much appreciated help not only with his research but with navigating many of the things related to day to day living: banking, parking permits, and how to enjoy life in the Northern Virginia area.

Among the things He enjoys doing in his free time are going to the gym, playing badminton, and trying new restaurants.

After earning his Ph.D., He would like to return to China to teach and continue his research at a university there.


B. Aditya Prakash moves to DAC’s Arlington location

Aditya Prakash (left) and his Ph.D. student Anika Tabassum (right) at DAC in Arlington.

Aditya Prakash, associate professor of computer science and faculty at the Discovery Analytics Center has moved from Blacksburg to DAC’s location at the Virginia Tech Research Center – Arlington.

“My work is frequently motivated by public health, urban computing, and web-related problems and this location is fertile ground for collaborations in these domains,” Prakash said. “Moving to the greater Washington D.C. metro area will help me further expand my research activities, due to its ‘one of its kind’ proximity to government agencies, companies, and hospitals/medical centers.”

In 2018, Prakash received a Faculty Early Career Development (CAREER) Award from the National Science Foundation to help improve national security and public health. His work has been also been funded through grants and gifts from the Department of Energy, the National Security Agency, the National Endowment for Humanities, and from companies like Facebook.

Prakash said that Virginia Tech’s planned Innovation Campus in Arlington and Alexandria would provide greater opportunity not only for research but also for recruiting talented students.

Prakash is currently advising five Ph.D. students in core computer science and other interdisciplinary programs.

Anika Tabassum, who is also a National Science Foundation research trainee in the UrbComp graduate certificate program, has joined Prakash at DAC in Arlington. Alex Rodriguez plans to make the move from Blacksburg in the Spring. They are working on urban computing and sequence mining and epidemiology and graph mining, respectively.

In addition to his research, Prakash is teaching a graduate level course, CS5834:Introduction to Urban Computing, this semester. The course, offered remotely to Blacksburg students as well, is a core course in the UrbComp certificate program administered through the Discovery Analytics Center and covers the fundamentals of the growing area of using analytics to tackle challenges  posed by increasing urbanization.

 

 

 


DAC Student Spotlight: Badour AlBahar

Badour AlBahar, DAC Ph.D. student in the Department of Electrical and Computer Engineering

Image is part of AlBahar’s research paper on “Guided Image-to-Image Translation with Bi-Directional Feature Transformation”

 

 

 

 

 

 

 

 

Next month Badour AlBahar will travel to Seoul, Korea, to present a paper, “Guided Image-to-Image Translation with Bi-Directional Feature Transformation” at the premier International Conference on Computer Vision (ICCV).

A second year Ph.D. student in the Department of Electrical and Computer Engineering, AlBahar said she is “extremely fortunate to be able to explore and broaden my knowledge in computer vision and machine learning with Professor Jia-Bin Huang and my fellow colleagues at the Discovery Analytics Center. Working with people from different backgrounds and experiences has enabled me to grow and learn in ways I never knew possible.”

The research she will share at ICCV is a collaboration with Huang.

AlBahar’s interests lie in video and image processing and more specifically, generative modeling.

“In generative modeling, we try to synthesize realistic data. In my recent paper, I try to generate a realistic image from a given input image respecting some kind of constraints set by a guidance signal,” she said. “For example, given an image of a person, I would generate an image of that same person in a different pose specified by a guidance signal.”

AlBahar was introduced to her research focus area during her first semester as a master’s degree student at Virginia Tech.

“I took a computer vision course and was extremely fascinated by the field,” she said. “I loved how it was fast evolving and rapidly developing with a lot of potential for innovative inventions and applications. I remember thinking I had finally found my niche.”

AlBahar vividly remembers something else about her first Computer Vision class in the fall of 2016.

“Professor Huang asked each of us to say something interesting about ourselves,” said AlBahar,  “I said I was pregnant and due in the middle of the semester. All the students clapped and cheered. It was very encouraging and heartwarming.”

Her daughter, AlZain, was born in October 2016.

Her son, Saleh, was born last month.

“My mother has always been my inspiration. She finished her Ph.D. in mathematics while raising her children,” said AlBahar.

“Now, having children of my own, I know it is not easy. However, I believe that having well set plans for the day makes it a bit easier. I take it one day at a time,” she said. “I always say that children are a motivation. I aspire to be a successful and renowned researcher and a role model for my children.”

After graduation, projected for 2021, she plans to return to her home country of Kuwait to teach in the Department of Computer Engineering at Kuwait University, from which she received her bachelor’s degree.

“ I want to transfer the knowledge I have gained and my experiences to my community,” AlBahar said.


DAC Student Spotlight: You Lu

You Lu, DAC Ph.D. student in computer science

You Lu knew that the Discovery Analytics Center would be a great fit for his research when he applied to the Virginia Tech Ph.D. program in computer science. “I had heard about Professor Bert Huang and knew that he and I were working in a similar research area — graphical models,” said Lu. “I felt I could learn a lot from him and I am so glad he agreed to be my advisor.”

In June, You attended the International Conference on Machine Learning (ICML) in Long Beach, California, and presented his collaborative research with Huang, “Structured Output Learning with Conditional Generative Flows,” at the Workshop on Invertible Neural Nets and Normalizing Flows.

Lu and Huang also collaborated on “Block Belief Propagation for Parameter Learning in Markov Random Fields,” a paper in proceedings at the AAAI Conference on Artificial Intelligence earlier this year.

Lu’s research at DAC combine his interests in both theoretical analysis and in applying models to practical problems. “To solve structured prediction problems, you need to not only design models that work well in practice but also theoretically analyze their performance,” Lu said.

“At DAC I have the opportunity to work with many talented and great researchers and I have access to very good computing resources that are valuable to my work,” Lu said.

He focuses on generalized supervised learning that involves predicting structured objects rather than a scalar. He also resorts to other techniques like variational inference, graphical models, and deep generative models to solve structured prediction problems.

Lu said his research is relevant to many real world applications in natural language processing, computer vision, network science, etc. “One common application is image completion, filling out the missing parts of corrupt images,” Lu said.

Lu received a bachelor’s degree in computer science from Jilin University in China and a master’s degree in computer science from the University of Colorado Boulder.

Projected to graduate in 2021, Lu plans to pursue a career in research as a university professor or a research scientist in a laboratory.

 


DAC Student Spotlight: Prerna Juneja

Prerna Juneja, DAC Ph.D. student in the Department of Computer Science

With a master’s degree in computer science from Indraprastha Institute of Information Technology (IIIT) Delhi, Prerna Juneja joined Dell EMC where, for three years, she worked for the company’s flagship product VPLEX, a storage virtualization appliance that provides continuous availability and data mobility. She garnered four awards for her work there: the Dell Champion Award in 2018, and Excellence@Dell Bronze Award in 2018, 2017, and 2016.

Deciding to pursue a Ph.D. in computer science, Juneja said she chose Virginia Tech over other universities because of its faculty who work in cutting-edge interdisciplinary research in the area of human computer interaction. Her advisor is Tanushree Mitra.

Juneja’s research interests are broadly based in computational social science, natural language processing, and machine learning.

“Search engines are the primary gateways of information. Despite their importance in selecting, ranking, and recommending what information is considered most relevant for us, there is no guarantee that the information is credible. Thus, as a part of my research I empirically audit search systems for misinformation and investigate user attributes, user actions, and events that might have an effect in amplifying this misinformation,” Juneja said.

“Being a student at the Discovery Analytics Center has given me an opportunity to interact with other students pursuing similar interdisciplinary research,” said Juneja. “People here are very helpful, always ready to offer advice and encouragement when you really need it.”

Juneja said her current research on algorithmically curated misinformation is important since exposure to inaccurate search results, coupled with unwavering trust placed in search engines, can lead to a misinformed citizenry.

“I believe my research will inform the need for building search engines that retrieve and present results ranked according to both relevance and credibility,” she said.

Juneja received a scholarship to attend the  CRA-W Grad Cohort Workshop for women in April 2019.

In previous research, Juneja studied YouTube for misinformation and investigated whether personalization (based on age, gender, geolocation, or watch history) contributes to amplifying this misinformation. She also investigated whether content moderation practices on Reddit sub-communities align with principles of transparency outlined in the guidelines issued by Santa Clara principles on Transparency and Accountability in Content Moderation. For this project, Reddit moderators were interviewed to get their view about different facets of transparency and to determine why lack of transparency is a widespread phenomenon.

Passionate about both teaching and research, Juneja has set her sights on a career in academia. Her projected graduation date is 2022.

 


Davon Woodard heads to South Africa for first phase of research on social networks in minority communities

During an earlier trip to Johannesburg, Davon Woodard explored one of the city’s most popular gathering places, the Neighbourgoods Market.

Davon Woodard will spend the 2019 Fall academic semester as a visiting scholar at the University of the Witswatersrand in Johannesburg, South Africa.

There, the Ph.D. student in the planning, governance, and globalization program in the School of Public and International Affairs and a research trainee in the National Science Foundation-sponsored Urban Computing (UrbComp) Certificate program administered through the Discovery Analytics Center, will conduct the first phase of research for his dissertation. He is comparing both online and offline social networks in two historically marginalized black communities — Johannesburg and Bronzeville in Chicago, Illinois — to get a better understanding of their structure, practices, and effects.  Click here to read more about Davon’s research on social networks.


Sneha Mehta to attend Deep Learning and Reinforcement Learning Summer School in Canada

DAC Ph.D. student Sneha Mehta is an intern this summer at Netflix headquarters in Los Gatos, California.

Sneha Mehta has been selected to attend the Deep Learning and Reinforcement Learning Summer School (DLRLSS), from July 24 to August 2, in Edmonton, Alberta, Canada.

Mehta is a Ph.D. student in computer science at the Discovery Analytics Center, advised by Naren Ramakrishnan. In May, she began a summer internship as a data scientist at Netflix headquarters in Los Gatos, California, where she is researching novel methods to improve machine translation for subtitles.

“I applied to the Summer School because deep learning and reinforcement learning are very relevant to my work at problem solving both at DAC and at Netflix,” said Mehta. “Hearing directly from some of the pioneers in the field will be a great – and invaluable – experience.”

The 2019 DRLSS is hosted by the Canadian Institute For Advanced Research (CIFAR) and the Alberta Machine Intelligence Institute (Amii), with participation and support from the Vector Institute and the Institut québécois d’intelligence artificielle (Mila).

The program brings graduate students, post-docs, and professionals together to cover the foundational research, new developments, and real-world applications of deep learning and reinforcement learning.

Mehta will present a poster of her collaborative work at DAC, Event Detection using Hierarchical Multi-Aspect Attention which she presented at The Web Conference in May.

“The Summer School also provides a great opportunity to network with Ph.D. students, postdocs and industry professionals from all over the world who apply these technologies to a variety of fields, including recommendation systems, physics, 3D printing, marine biology, natural language processing, computer graphics, medical image analysis, neuroscience, epidemics, computer vision, and drug discovery,” Mehta said.

At the conclusion of the program, Mehta will continue her internship at Netflix until August 14, and then return to Virginia Tech for the Fall semester.

 


Tanushree Mitra among first to receive research grant providing access to Facebook data

(From left) Tanushree Mitra and Ph.D. students Prerna Juneja, Shruti Phadke, and Md Momen Bhuiyan participated in a training workshop at Facebook earlier this month.

Tanushree Mitra, assistant professor of computer science in the College of Engineering and a faculty member at the Discovery Analytics Center, has received a Social Media and Democracy Research Grant, giving her access to Facebook data to study how misinformation and other problematic content spread on the platform. This is the first time Facebook has given academics access to its data.

Mitra is among 60 international researchers — and one of only two women principal investigators — to receive the grant, funded by a number of leading foundations and administered through a Social Science One and Social Science Research Council collaborative partnership that bridges the gap between academics and the private sector.  Click here to read more about Mitra’s grant.


Google Faculty Research Award supports work in detecting human-object interaction in images and videos

Jia-Bin Huang, assistant professor of ECE and DAC faculty member

Jia-Bin Huang, assistant professor in the Bradley Department of Electrical and Computer Engineering and a faculty member at the Discovery Analytics Center, has received a Google Faculty Research Award to support his work in detecting human-object interaction in images and videos.

The Google award, which is in the Machine Perception category, will allow Huang to tackle the challenges of detecting two aspects of human-object interaction: modeling the relationship between a person and relevant objects/scene for gathering contextual information and mining hard examples automatically from unlabeled but interaction-rich videos.  Click here to read more about Jia-Bin’s Google Award.


Summer months take DAC students to professional internships and jobs across the country

DAC Ph.D. students Ping Wang (left) and Tian Shi are in Richland, Washington, this summer, where they are interns at the Pacific Northwest National Laboratory.

A number of graduate students at the Discovery Analytics Center have opted for internships and jobs at companies and national laboratories across the country this summer as a way of both benefiting their own research and gaining real world experience.

Following is a list of where they are for the next few months:

Aman Ahuja, a Ph.D. student in computer science, is an applied scientist intern at Amazon in Palo Alto, California. He is on the Amazon Search Team, researching product search techniques. His advisor is Chandan Reddy.

Tyler Chang, a Ph.D. student in computer science, has begun a six-month appointment at Argonne National Laboratory in Chicago, Illinois. He is one of 70 graduate students who received an appointment from the U.S. Department of Energy (DOE) Office of Science Graduate Student Research (SCGSR) to work on his thesis. The goal is to produce a portable multi-objective optimization software which Argonne could utilize in the future. Chang’s advisor is Layne Watson.

Jinwoo Choi, a Ph.D. student in electrical and computer engineering, is a research intern at NEC Labs America, San Jose, California, working on domain adaptation for video. Choi’s advisor is  Jia-Bin Huang.

Chen Gao, a Ph.D. student in electrical and computer engineering, is a research intern on a video completion project at Facebook in Seattle, Washington. He is working on an algorithm that synthesizes missing regions of videos. His advisor is Jia-Bin Huang.

Liuqing Li, a Ph.D. student in computer science, is on the Content Science team at Yahoo! Research in Sunnyvale, California, working on document recommendation through reinforcement learning. His advisor is Edward Fox.

Sneha Mehta, a Ph.D. student in computer science, is a data science intern at the Netflix headquarters in Los Gatos, California. She is researching novel methods to improve machine translation for subtitles. Her advisor is Naren Ramakrishnan.

Shruti Phadke, a Ph.D. student in computer science, is an intern at the Oak Ridge National Laboratory in Oak Ridge, Tennessee. She is working on developing scalable machine learning and Natural Language Processing (NLP) algorithms to detect public sentiment in news and social media. Her advisor is Tanushree Mitra.

Esther Robb, a master’s degree student in electrical and computer engineering, is a research intern at Google in Mountainview, California, where she is working on facial recognition. Her advisor is Jia-Bin Huang.

Alexander Rodriguez, a Ph.D. student in computer science, is a research intern at WalmartLabs in Sunnyvale, California. His advisor is B. Aditya Prakash.

Dhruv Sharma, a master’s student in computer science, is working at Kitware, Inc., in Carborro, North Carolina. As a research and development intern, Sharma’s work includes some medical image processing/machine learning tasks; mining EHR data for prediction of risk, procedure outcome, or other events; and analyzing training needs of healthcare providers. He is advised by Chandan Reddy.

Tian Shi, a Ph.D. student in computer science, is an intern at Pacific Northwest National Laboratory in Richland, Washington, where he is working on machine comprehension and question-answering on clinical notes in the healthcare domain. His advisor is Chandan Reddy.

Shih-Yang Su, a Ph.D. student in electrical and computer engineering, is a research intern at Borealis AI in Vancouver, Canada, working on graph convolution for structural prediction. His advisor is Jia-Bin Huang.

Deepika Rama Subramanian, a master’s student in computer science, is a mobility intern at Lam Research, Fremont, California, working on designing and developing an end-to-end mobile application for field engineers at Lam Research. Her advisor is Tanushree Mitra.

Anika Tabassum, a Ph.D. student in computer science, is a research intern at the Oak Ridge National Laboratory in Oak Ridge, Tennessee, where she will be applying data mining and visualization skills in two U.S. Department of Energy (DOE) projects: “Reynolds Landing Research” and “North American Energy Resilience Model.” Her advisor is B. Aditya Prakash.

Sai Sindhura Tipirneni, a master’s student in computer science, is working in the Quantum Computing Lab at Oak Ridge National Laboratory in Oak Ridge, Tennessee. Her advisor is Chandan Reddy.

Ping Wang, a Ph.D. student in computer science, is an intern at Pacific Northwest National Laboratory in Richland, Washington, where she is working on question answering on electronic medical records using Natural Language Processing (NLP) techniques. Wang’s advisor is Chandan Reddy.

Sirui Yao, a Ph.D. student in computer science, is a research intern at Google AI in New York City, where she is studying noise and bias in dynamic recommender systems. Her advisor is Bert Huang.

Ming Zhua Ph.D. student in computer science, is at Amazon in Seattle, Washington. She is an applied scientist intern for Amazon Comprehend Medical, working on Natural Language Processing on medical corpora using deep learning. Zhu’s advisor is Chandan Reddy.

Yuliang Zou, a Ph.D. student in electrical and computer engineering, is a research intern at NEC Labs America in San Jose, California, where he is working on unsupervised scene structure learning. His advisor is Jia-Bin Huang.


Congratulations to our Ph.D. and master’s degree Spring graduates at the Discovery Analytics Center!

DAC graduates include (left to right): Xuchao Zhang with advisor Chang-Tien Lu in the National Capital Region; and Elaheh Raisi with Bert Huang and Yufeng Ma hooded by Ed Fox, both in Blacksburg.

The Discovery Analytics Center is pleased to announce that five of their Ph.D. and four of their master’s degree students celebrated graduation from Virginia Tech last weekend at Commencement ceremonies in Blacksburg and in the National Capital Region.

“It is always bittersweet to bid our students farewell, but we wish them all the best. We know and appreciate how hard they have worked to achieve the high goals they set for themselves and look forward to following their successful careers in academia and industry,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the center.

 

Ph.D. graduates

Sorour E. Amiri, advised by B. Aditya Prakash, received a Ph.D. in computer science.
Her research interests are large-scale graph mining, data mining, and applied machine learning and the title of her dissertation is “Task-specific Summarization of Networks: Optimization and Learning.” She is joining the Google search ad team.

Minghan Chen, co-advised by Layne Watson, received a Ph.D. in computer science. Her research interest is computational cell biology and her dissertation title is “Stochastic Modeling and Simulation of Multiscale Biochemical Networks.” She joins the Computer Science Department at Wake Forest University as assistant professor.

Yufeng Ma, co-advised by Weiguo (Patrick) Fan and Edward Fox, received a Ph.D. in computer science. Ma’s research interests are computer vision, Natural Language Processing (NLP), and deep learning and his dissertation title is “Going Deeper with Images and Natural Language.” Ma is joining Verizon Media (Yahoo! Research) as a research scientist focusing on personalized recommendations.

Elaheh Raisi, advised by Bert Huang, received a Ph.D. in computer science. Her research interests are machine learning, weakly supervised learning, and computational social science and her dissertation title is “Weakly Supervised Machine Learning for Cyberbullying Detection.”

Xuchao Zhang, advised by Chang-Tien Lu, received a Ph.D. in computer science. His research interests are data mining, machine learning, and Natural Language Processing (NLP) and his dissertation title is “Scalable Robust Models Under Adversarial Data Corruption.”  Zhang joins NEC Labs America as a researcher. In that position he will work to fully understand the dynamics of big data from complex systems; retrieve patterns to profile them; and build innovative solutions to help end user managing those systems.

Master’s graduates

Raja Venkata Satya Phanindra Chava, advised by Edward Fox, received a master of engineering degree. His research interests are text summarization using deep learning and Natural Language Processing (NLP) and his project title is “Natural Language Processing Techniques for Comprehending Legal Depositions.” Chava is joining Walmart in Reston, Virginia, as a software engineer and will work on big data analysis to manage the supply chain and personalize the customer’s shopping experience.

Supritha B. Patil, advised by Edward Fox, received a master of science degree in computer science. Patil’s research interest is Natural Language Processing (NPL) and her thesis title is “Analysis of Moving Events Using Tweets.” She will be working as a software developer.

Adithya Upadhya, advised by Edward Fox, received a master’s in computer science. His research interests are machine learning and high performance computing and his project title is “A General Web Platform Summarizing Text and Documents.”

Xinfeng Xu, advised by B. Aditya Prakash, received a master’s degree in computer science. His research focused on modeling and predicting incidence and the title of his thesis is “Modeling and Predicting Incidence: Critical Systems Failures and Flu Infection Cases.” He also received the 2019 MS Research Award from the Department of Science. Xu is also a Ph.D. student in physics in the College of Science and will continue his research in that field.

 

 

 

 


DAC student Xinfeng Xu garners 2019 Computer Science MS Research Award

Xinfeng Xu, DAC Master’s student in computer science

Xinfeng Xu, a master’s student in the Discovery Analytics Center, received the Computer Science MS Research Award at the CS Awards Banquet last night.

The award recognizes the best MS thesis in CS with consideration to novelty of idea; quality of resulting publications; effectiveness of writing; and contributions/impact to the field overall.

Xu’s computer science research primarily focused on modeling and predicting incidence in two cases that take dynamics of propagation into account. He has defended his master’s dissertation, “Modeling and Predicting Incidence: Critical Systems Failures and Flu Infection Cases,” and will receive his degree from the Department of Computer Science later this month.

Aditya Prakash, Xu’s advisor, nominated him for the research award.

“This award is a nice recognition of Xinfeng’s work,” Prakash said. “His thesis offers new algorithms and models for two tough real-world problems — vulnerability of critical infrastructure systems and influenza surveillance. We are already using algorithms from his thesis in a toolkit being developed with the Oak Ridge National Laboratory for power systems, and also as part of Virginia Tech’s submission to the ongoing 2018/19 CDC FluSight challenge.”

Xu is also a Ph.D. student in physics in the College of Science and will continue his research in that field exploring the mystery of Active Galactic Nuclei (AGN). He is projected to complete the Ph.D. program in Spring 2020.


Amazon Research Award supports developing algorithms that tackle unfairness in recommendation engines

Bert Huang, DAC faculty member and assistant professor of computer science

Why would a recommendation engine not suggest computer science classes to a female college student interested in that field of study?

According to Bert Huang, assistant professor of computer science in the College of Engineering and a faculty member at the Discovery Analytics Center, there are a few reasons. The engine may have trained from data representing the existing gender imbalance in computer science, unfair patterns may have inadvertently emerged from the mathematical nature of its learning algorithm and model, or there may be a less-visible or harder-to-detect process in place.  Click here to read more about Bert’s work.


Software platform engages communities in school rezoning decisions

Left to right: Colin Flynn, Vicki Keegan, and Susan Hembach from Loudoun County Public Schools meet at the Discovery Analytics Center with Ph.D. students Andreea Sistrunk, Subhodip Biswas, and Fanglan Chen to discuss how Redistrict is helping to establish school attendance zones.

School rezoning decisions often cause emotional stress for families and communities for a variety of reasons.

Parents worry about continuity of programs and activities at a new school, the toll it might take on their children’s friendships, and modes of transportation. School officials, administrators, and staff want to ensure that all students have equitable access to educational programs and facilities. Almost everyone is concerned about the impact a particular school attendance zone will have on traffic patterns, especially at opening and closing times.

Redistrict, an online interactive platform developed at the Discovery Analytics Center at Virginia Tech, is trying to reduce that stress by getting parents and other stakeholders more involved in the process. The platform uses data analytics and machine learning to help them better understand school rezoning plans and their potential effect on the community; share their comments and concerns about proposed plans; propose changes to boundaries; and even create their own plans. Click here to read more about the Redistrict platform.


DAC Student Spotlight: Payel Bandyopadhyay

Payel Bandyopadhyay, DAC Ph.D. student computer science

Payel Bandyopadhyay is trying to understand the role of 3D immersive environment (an artificial, interactive, computer-created scene or “world” within which a user can immerse themselves) for sensemaking in textual data.

According to Bandyopadhyay, prior work at Virginia Tech by her advisor Chris North and others has shown the part that 2D space plays in sensemaking. Her current research investigates 3D immersive environments to determine if they provide any additional benefit or not.

“When I started my Ph.D. in computer science, I was looking for a topic at the cutting edge of visualization research,” she said. “Dr. North helped me be a part of this project and I am very intrigued by it. His expertise in the information visualization field is one of the reasons I was drawn to the Discovery Analytics Center, where I can work at the intersection of human computer interaction and analytics.”

Bandyopadhyay earned a master’s degree in computer science from the University of Helsinki, Finland, specializing in networking and services. While earning her master’s degree, she worked as a full-time graduate assistant.

Springer has published two of her studies — “Navigating Complex Information Spaces: A Portfolio Theory Approach,” at the 2015 International Workshop on Symbiotic Interaction, on which Bandyopadhyay was first author, and “User Model in a Box: Cross-System User Model Transfer for Resolving Cold Start Problems,” at the 2015 User Modeling, Adaptation and Personalization (UMAP) conference, on which she was a collaborator.

In Summer 2018, she interned with the Advanced Technology Group at UPS headquarters in Atlanta, Georgia, where she worked on building several data visualization tools for UPSers, the official feed for UPS employees and fans. As of this writing, she is planning to intern at UPS again this summer.

Bandyopadhyay, who cites swimming, hiking, and dancing among her favorite spare time activities, is projected to graduate in May 2021 and hopes to continue research in academia or industry.


DAC Student Spotlight: Lulwah AlKulaib

Lulwah AlKulaib, DAC Ph.D. student in computer science

While a master’s degree student in computer science at George Washington University, Lulwah AlKulaib would look for published papers in high impact factor journals and highly respected, top rated conferences matching her field of interest, machine learning.

“This is where I first learned about the Discovery Analytics Center, the research being done there, and that it was located in northern Virginia as well as in Blacksburg,” she said.

 While still at George Washington, AlKulaib had an internship, unrelated to machine learning, at the Advanced Research Institute, which, like DAC, is located at the Virginia Tech Research Center in Arlington.

“There I met other Ph.D. students and because of my research interests, they encouraged me to consider DAC as a Ph.D. option,” said AlKulaib.  She also found that Chang-Tien Lu, whose research interests were aligned with hers, was an associate director at DAC and now, he is her advisor.

“Dr. Lu is amazing at guiding us on how to approach problems and he creates a network of students who learn from and support each other,” she said. “And the resources available for students’ benefit and growth at DAC is beyond what I imagined.”

 AlKulaib’s work focuses on social media analysis, machine learning, and Natural Language Processing (NLP). NLP centers on enabling computers to understand and process human language (natural language). Machine learning builds systems that can learn from experience (existing data), she said. By combining the two, she can start building systems that can learn how to understand languages.

Currently, she is working on news classification, enabling a machine learning model to understand a news article and then labeling the article into one of two existing classes.

“This field has lots of potential and multiple areas of research and my interest lies in Arabic,” AlKulaib said. “There are 22 Arabic speaking countries that all speak the same language — called Standard Arabic — taught in school, written in religious scriptures, and used for formal communication that includes news and work documents. But each country also has at least one informal dialect used for daily communication that is a variant of Standard Arabic named after the country or city where it is spoken. Lately, dialects have started appearing in written form on social media platforms which adds to the complexity of approaching any Arabic NLP problem, but makes it more interesting too.”

Her interest in this area stemmed from her first job after graduating with a bachelor’s degree in computer science from Gulf University for Science and Technology in Kuwait. As researcher and programmer at Kuwait Institute for Scientific Research (KISR) at the Technology Applications for Special Needs section, she provided clients with Arabic assistive technology.

“While working on a tool for speech disorders, I learned a lot about NLP and its difficulties in Arabic. That was the beginning of it all,” she said.

AlKulaib collaborated on Weaponized health communication: Twitter bots and russian trolls amplify the vaccine debate, published in the American Journal of Public Health in August 2018.

At the 2018 International Conference on Social Computing, Behavioral-Cultural Modeling, Prediction and Behavior Representation in Modeling and Simulation, she presented “Detecting and characterizing bot-like behavior on twitter.”

She and project partner, Abdulaziz AlHamdani, also a DAC Ph.D. student, are working on a paper about collecting datasets with minimized bias using Twitter. Basically, they are formulating a collection framework to ensure that the dataset has minimal bias, caused by using social media as a source for data collection. They will submit the paper to a conference in early June and she will present in November if accepted.

When she finds some free time, AlKulaib enjoys photography, traveling, socializing and meeting new people.

She is projected to graduate in 2021.

 

 


Discovery Analytics Center study sheds light on what turns a peaceful protest into a violent one

Protest in Brazil

Protests are an increasingly common occurrence, but only a small percentage of them turn violent. In a collaborative study led by the Discovery Analytics Center with the University of California, San Diego, and George Mason University, a team of researchers set out to uncover triggers that foretell violence by crowds.

Gathering data from thousands of online news sources in five Latin American countries — Argentina, Brazil, Colombia, Paraguay, and Venezuela — the researchers used the characteristics of past events to develop new methods that forecast the occurrence of violent crowd behavior in advance.

“Crowd violence is not generally initiated by one factor but, often, is a culmination of outrage over a stream of preceding unresolved public issues or events,” said Yue Ning, lead author of the study, who was a Ph.D. graduate in computer science from the Discovery Analytics Center at the time of the study and now an assistant professor at Stevens Institute of Technology. “Our study showed that before a violent protest in any of these countries, other protests and strike events, even if peaceful, occurred during the prior week.”

“The fact that violent protest can be modeled before it happens is an important finding of the study,” said David Mares, Institute of the Americas Chair for Inter-American Affairs and professor of political science at the University of California, San Diego. “The link between the act of protesting and violent behavior in a protest has been difficult to understand because so many factors are operating at the same time. Our model gives us confidence that it will be possible to develop a better understanding of the factors that transform peaceful protest into violent confrontations.”

The study was designed to give governments, law enforcement, and community organizations insights that can help them support the right to peaceful gatherings, mitigate the level of frustration and anger that people who have been in many recent protests experience, and reduce the risk of violence.

“Being able to forecast violent events can help policymakers make better decisions about how to deal with protests,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science and director of the Discovery Analytics Center. “And understanding triggers is important because any effort to decrease the probability of a violent gathering without understanding the dynamics that differentiate violent from non-violent events can lead to measures that have the opposite effect.”

For example, he said, a significant show of force with police or the military at the first sign of protest can intimidate and frustrate protesters rather than make them feel protected. If such intimidation and frustration build into anger, the likelihood of violence increases during the next such gathering.

Huzefa Rangwala, professor of computer science at George Mason University, said the study also showed that events can be influenced by what is happening in different locations. “One might have thought that people would be most affected by what happens locally, but our data suggests that those protesters prone to violence reflect upon national and not just local experiences when voicing grievances and increasing frustrations that lead to violence.”

In addition to Ning, Mares, Ramakrishnan, and Rangwala, the research team included Sathappan Muthiah, a Ph.D. student in the Discovery Analytics Center majoring in computer science.

Read the full study, “When do Crowds turn Violent? Uncovering Triggers from Media.”

 

 

 


DAC Student Spotlight: Tyler Chang

Tyler Chang, DAC Ph.D. student in computer science

The spring semester has brought some good news for Tyler Chang, a Ph.D. student at the Discovery Analytics Center.  In June, he will begin a six-month appointment at Argonne National Lab in Washington, D.C., where he will continue to work on his dissertation while applying his work to a new set of problems relevant to the U.S. Department of Energy.

Chang, a computer science major specializing in numerical analysis, is focusing his research on interpolation and nonconvex optimization.  His advisor is Layne Watson.

The interpolation problem is to predict values between data points. “Given the total revenue earned by some small businesses and numerical descriptions of their marketing strategies, one might interpolate to predict revenue that will be earned by a new business with its own marketing strategy,” said Chang.

“The optimization problem is to find a best configuration by choosing where to sample new data points. For example,” he said, “when designing an aircraft, each design produces some amount of lift. So, an aircraft engineer might use optimization to search for the particular design that produces the maximum lift.”

His research is partially funded by the VarSys project, an interdisciplinary effort to understand and model performance variance in computer systems. The motivation for this project is that small fluctuations in the throughput, energy consumption, etc., of large machines can have significant consequences for computer system performance, behavior, and even security.

Chang said that while this may seem far removed from his research, the VarSys project can boil down to gathering performance data and then predicting performance statistics for new system configurations (the interpolation problem) and even searching for system configurations that minimize or maximize performance statistics (the optimization problem).

His bachelor’s degree from Virginia Wesleyan College is in mathematics and computer science. While an undergraduate, Chang  held a number of research internships spanning computer vision, circuit/hardware design, information visualization, autonomous driving, and parallel computing.

“As a double major, I was always looking for opportunities to apply both of my skills,” said Chang. “I discovered numerical analysis while working at Old Dominion University on a NASA grant involving computational fluid dynamics, work I initially found to be extremely challenging. But it offered the perfect marriage of passion for mathematics and computer science. My current research in interpolation and optimization allows me to channel my interest in those two fields into helping to solve a wide variety of engineering design and data science problems.”

Chang is first author on three conference papers: “Computing the Umbrella Neighbourhood of a Vertex in the Delaunay Triangulation and a Single Voronoi Cell in Arbitrary Dimension,” IEEE Southeast Con 2018; “A Polynomial Time Algorithm for Multivariate Interpolation in Arbitrary Dimension via the Delaunay Triangulation,” in Proceedings of the ACMSE 2018 Conference; and “Predicting System Performance by Interpolation Using a High-dimensional Delaunay Triangulation,” in Proceedings of the High Performance Computing Symposium (HPC ’18), Society for Computer Simulation International. Chang has also co-authored four additional conference papers.

“I love the interdisciplinary aspects of my research at DAC,” said Chang. “Through collaboration, I learn about, and even contribute to, cutting-edge research in statistics, computer systems, computer security, engineering, and other fascinating fields, all while continuing to hone my skills in numerical analysis.”

When Chang has free time he enjoys weight lifting and playing 80s and 90s rock tunes on his keyboard. While he played varsity tennis as an undergraduate, his interest in tennis is now as a fan of his sister Sophie, who is currently a top 500 professional tennis player.

Projected to graduate in 2020, Chang said he would be happy with a career in either industry or academia. “But,” he said, “having great experiences working for government labs, I would consider a position with a national lab to be my top career goal.”


Winning Blockchain Challenge team includes DAC student Arjun Choudhry

From left to right: Ikechukwu Dimobi, Arjun Choudhry, and Zachary Gould

A three-member student-driven team that includes Arjun Choudhry, a student at the Discovery Analytics Center, has won first place in the design phase of the Virginia Tech Blockchain Challenge led by the Department of Computer Science and made possible in part through a generous gift from Block.one, a leader in providing high-performance blockchain solutions. The award carries a $1,000 prize.

Choudhry is a master’s student in computer science advised by Naren Ramakrishnan. His teammates are  Zachary Gould, a Ph.D. student in building construction advised by Georg Reichard; and Ikechukwu Dimobi, a master’s student in electrical engineering advised by Saifur Rahman.

The challenge posed this problem for the team: Virginia Tech plans to expand its campus and double the current population of students and staff by 2047. This growth will exacerbate the current challenge of meeting peak-energy demands while accommodating an increasing amount of distributed energy resources such as solar panels. Utilities must learn how to encourage energy efficient practices to reduce demand and how to effectively coordinate interaction between consumers, renewable energy sources, and the grid. With earnings currently communicated on existing monthly billing cycles, existing solutions such as net-metering, feed-in-tariffs and demand response are neither transparent nor resolute enough.

In their winning design solution, Choudhry, Gould, and Dimobi developed a secure energy distribution platform for multi-family apartment buildings and neighborhoods that share a solar panel array.

They use smart contracts on the EOSIO platform to transparently and dynamically distribute revenue using tokens. At every timestep, each resident is allocated tokens worth a fair portion of total energy production or loses tokens based on consumption, hence incentivizing energy efficient behavior. The total net energy supply or demand from the community would then be transacted with the local utility.

This paves the way towards community scale solar where residents can equitably share in both costs and profits, Choudhry said.

The three students are now working on an April 24 deadline to submit a plan to implement their design. The second phase winner will be notified on May 2.

Judging for both the first and second phases of the competition includes a maximum of five points each for use of EOSIO technology, creativity, impact, design and usability, and functionality.


DAC Student Spotlight: Shuo Lei

Shuo Lei, DAC Ph.D. student in computer science

A Ph.D. student in computer science, Shuo Lei is focusing her research on few-shot learning and robust model learning. She is advised by Chang-Tien Lu.

“The aim of AI is to train machines to do some of the work that people were needed to do previously,” said Lei. “The training process requires a large amount of labeled data. It is time intensive and there are significant labor costs in collecting and labeling all that data. Few-shot learning can be valuable in forwarding research because it reduces the training cost by using less labeled data to get the same – and sometimes even greater – accuracy in training results.

Lei has collaborated on two published papers that incorporate her current work: Robust Regression via Heuristic Corruption Thresholding and Its Adaptive Estimation Variation,” ACM Transactions on Knowledge Discovery from Data (TKDD) 2019; and “Robust Regression via Online Feature Selection under Adversarial Data Corruption,” proceedings of the IEEE International Conference on Data Mining (ICDM), Singapore, 2018.

Lei holds bachelor’s and master’s degrees in software engineering from Beihang University in China. The opportunity to work with professors who are expert in their fields, other talented students, and the northern Virginia location attracted her to Virginia Tech and the Discovery Analytics Center.

“I think the best thing about DAC is its abundance of academic resources,” Lei said. “I really enjoy working with everyone there. Dr. Lu provides a lot of support for my research and I have also learned from my lab members. They are very nice and helpful, always willing to offer suggestions whenever I have encountered a problem.”

Lei will spend the summer months engaged in her research at DAC.

Her previous interest in spatial temporal data mining, which included resident travel pattern analysis, is reflected in a collaborative paper, “Forecasting car rental demand based temporal and spatial travel patterns,” in 2017 IEEE Ubiquitous Intelligence, Cloud and Big Data Computing, Internet of People and Smart City Innovation.

When she has free time, Lei enjoys cooking, baking, traveling, and photography.

She is projected to graduate in May 2022.


DAC Student Spotlight: Moeti Masiane

Moeti Masiane, DAC Ph.D. student in computer science

Moeti Masiane’s initial interest in analyzing data grew even stronger when earning a bachelor’s degree in computer science from the University of the District of Columbia and then a master’s degree from Norfolk State University.

As he began to consider going on to a Ph.D. program in the same field, he was drawn to Virginia Tech and the Discovery Analytics Center. “The expert DAC faculty really made me want to be part of the team,” said Masiane, who is advised by Chris North.

He has been at DAC since 2016, where, he said, “I  am surrounded by talented faculty and students who are always willing to suggest new ways of solving data analysis-related challenges.”

Masiane has focused his Ph.D. research on data visualization. “In the process of trying to analyze large datasets, I realized that there is a research opportunity in trying to solve the big data visualization latency challenge,” he said.

Masiane is also a research trainee in the National Science Foundation-sponsored Urban Computing certificate program, an interdisciplinary program administered through DAC.

On March 28, he will present “Towards Insight Driven Sampling in Big Data Descriptive Analytics” at the UrbComp Seminar Series to discuss his work.

“Sampling is often used by authors of big data visualization systems to reduce big data into small data in order to make the visualization faster and the data compatible with traditional visualization techniques. The impacts of such sampling is known in statistical terms, but unknown in a visualization context,” he said.

His research uses a descriptive data analysis task performed by a class of more than 200 students to investigate and model the impact of sampling on insight, perception, visualization, and sampling errors.

“The ultimate goal is to increase the speed of big data visualization while helping system users make informed decisions on how to achieve this speedup,” Masiane said.

Two papers with his advisor and other collaborators were published in Informatics Journal in 2016: “Interactive Graph Layout of a Million Nodes” and “AVIST: A GPU-Centric Design for Visual Exploration of Large Multidimensional Datasets.”

Masiane, who is on schedule to graduate in May 2020, spent last summer working on his research and plans to do the same this year. His past experience includes working for Google and internships with Adobe and the Army Research Lab.


DAC Student Spotlight: Ming Zhu

Ming Zhu, DAC Ph.D. student in computer science

Ming Zhu learned about machine reading comprehension — making computers understand sophisticated natural language and be able to answer questions about what was read — while taking a graduate course at Carnegie Mellon University.

“After building a state-of-the-art Neural Question Answering (QA) model from scratch based on a research paper, my confidence grew in believing I could be a part of this future technology and pushed me further to focus my Ph.D. research in this area,” said Zhu.

Her interest in QA led Zhu, who holds a bachelor of engineering degree from the University of Science and Technology of China, to Chandan Reddy who is now her advisor at the Discovery Analytics Center’s National Capital Region location.

“Currently I am working on QA models with clinical notes as context. Clinical notes are a huge treasure of information if you make good use of them. They can help doctors make better medical decisions for their patients and help patients better understand their healthcare conditions,” Zhu said.

For example, she said, when deciding how to treat a new patient, a doctor might ask “How does rheumatic fever affect the heart when the patient is pregnant?” But, reviewing all the clinical notes in the database is impossible and a search engine cannot deal with this kind of query very well.

The AI-based Question Answering system that Zhu is working on can help by reading and comprehending the whole corpus, retrieving the most relevant articles or notes when given a human question, and selecting the most precise answers from them.

“My DAC advisor gives me a lot of freedom to explore while providing a lot of support to further my research,” said Zhu. “My lab mates are good partners with whom I can start heated discussions with at any time and the staff is nice and helpful.”

Zhu said she also likes the “city life” afforded by DAC’s northern Virginia location. In her free time she also enjoys cooking Chinese food, baking, and taking car trips with friends to visit places like Luray Caverns, Atlantic City, and New York City.

In May, Zhu will be traveling to The Web Conference 2019 in San Francisco to present a first-author paper, “A Hierarchical Attention Retrieval Model for Healthcare Question Answering.”

She is projected to earn her Ph.D. in computer science in May 2022 and plans to seek a position in academia.

 

 

 

 

 


DAC Student Spotlight: John Wenskovitch

John Wenskovitch, DAC Ph.D. student in computer science

John Wenskovitch’s research interest is centered around the idea of creating interactive visualization systems that learn from user interactions. This often takes the form of conducting exploratory data analysis on high-dimensional, numerical datasets and using a common visualization technique, 2D scatterplot, to project the data.

When asked if he could explain his work to someone not in the computer science field, Wenskovitch, a Ph.D. student at the Discovery Analytics Center, turned to the stars.

“Stars have a variety of properties including color, temperature, luminosity, mass, radius. If you project that high-dimensional data into the 2D scatterplot, the stars will naturally start to form groups because stars in the high-dimensional space also have groups like red giants, blue giants, white dwarfs, main sequence, etc.,” Wenskovitch said. “By manipulating the scatterplot, perhaps the system can learn that the user is interested in understanding the relationship between color and luminosity. After recognizing that interest, the system can learn that these high-dimensional attributes are important to the user and adapt the projection, with the result of something approximating a Hertzsprung–Russell diagram.”

How did he gravitate to this particular research area?

“I was the kid who was interested in everything. As a result, I had a really hard time choosing a major when I started undergrad. Eventually, I settled on computation because it is a very interdisciplinary field and gave me the ability to pursue a variety of interests,” said Wenskovitch. “Visualization is probably the most interdisciplinary subfield of computer science since its goal is to help everyone better understand their data. I have worked on visualization research projects with astronomers, cell biologists, nurses, statisticians, and artists.’

Wenskovitch earned a bachelor’s degree in software engineering with a minor in mathematics and a multimedia focus area from Gannon University and a master’s degree in computer science from the University of Pittsburgh. He said that his current research gives him the ability to exercise both his programming and mathematics skills, while also interacting with domain experts in a wide variety of fields.

“I like the fact that — as a student at the Discovery Analytics Center — I can walk down the hall whenever I want and have an interesting conversation with another grad student or faculty member who is working on something fascinating but completely different,” said Wenskovitch, who is advised by Chris North. “DAC has also provided a lot of interesting, inter-related problems to work on.”

Later this month, Wenskovich will present a poster, “Simultaneous Interaction with Dimension Reduction and Clustering Projections,” at the 24th International ACM Intelligent User Interfaces Conference in Los Angeles.

To date, Wenskovitch has given talks in nine countries on five continents, including papers at the IEEE VIS conference: “Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics” in 2017 and “The Effect of Semantic Interaction Foraging in Text Analysis” in 2018.

He is currently working with his supervisor and colleagues from a Summer 2018 internship at  Fuji-Xerox Palo Alto Laboratory (FXPAL) to turn the work they did on building a software visualization assistant to help with navigation and visual debugging in computational notebooks into a paper for VIS 2019.

He has also taught college classes since 2012, including at Virginia Tech, and was a visiting assistant professor at Allegheny College before joining DAC in Fall 2016. He is projected to graduate in Summer 2019 and would like to remain in academia.

Wenskovitch said he likes living in Blacksburg, with its easy access to mountain hiking. “It is a small town where I can see the Milky Way from my driveway, but it is still quite cosmopolitan,” he said.

 

 

 

 

 

 


DAC Student Spotlight: Thomas Lux

Thomas Lux, DAC Ph.D. student in computer science

Thomas Lux does not hesitate when it comes to setting long-term goals.

“After graduation I would like to work somewhere that allows me to devote my time to pursuing research in artificial general intelligence,” he said. “I can easily see myself at an industry/government lab, in academia, or in a small startup. I will be happy as long as I get to contribute to the creation of super-human intelligent algorithms that can benefit people in society.”

Lux, a computer science student in the Discovery Analytics Center and a research trainee in the National Science Foundation-sponsored Urban Computing certificate program, an interdisciplinary program administered through DAC, would also like to use his data analytics skills to collaborate and contribute to his fiancé’s work in neuropsychological assessment. (She is pursuing a Ph.D. in clinical neuropsychology at Saint Louis University.)

In choosing a Ph.D. program, he was particularly interested in working with faculty like his advisor, Layne Watson, who have strong backgrounds in mathematics and optimization, and whose research is grounded in practical applied problems.

“I think the best way to solve problems is by learning to combine existing theory with real-world constraints in order to develop new theory tailored for specific applications,” Lux said.

Lux’s research focuses on applied approximation, numerical analysis, and nonparametric statistics. He is part of the VarSys team that creates models to help understand and manage computational performance variability across the computer system stack.

Petascale — and inevitably exascale — computing comes with many hurdles, he said, including immense losses in performance that result from interactions between parts of computers. The team collects data and builds models that address questions like: How do you configure an operating system to minimize energy consumption? What file and record sizes should be used to minimize read throughput variance? What CPU cache hierarchy is least vulnerable to side channel attacks?

Lux is committed to contributing to this field of study and this summer will be his third consecutive doing research at VarSys.

“Although the systems-oriented application that I work on may sound far off, the underlying mathematical concepts are surprisingly similar. In order to invent intelligent learning algorithms, we must understand the limits and maximize the performance of models we build over data. My theoretical work for systems is laying a foundation of robust, mathematically justified, and explainable algorithms for creating learning machines,” Lux said.

Lux’s research as first author has been included in a number of conference proceedings. Among them are: Nonparametric Distribution Models for Predicting and Managing Computational Performance Variability, IEEE SoutheastCon 2018; Predictive Modeling of I/O Characteristics in High Performance Computing Systems, Association of Computing Machinery High Performance Computing Symposium, April 2018; and Novel Meshes for Multivariate Interpolation and Approximation, 2018 Association of Computing Machinery Southeast Conference.

He has also coauthored a number of papers.

When not busy working on his research, Lux enjoys active sports — soccer, frisbee golf, and racquetball — and hiking the mountains of southwest Virginia. Formally trained in jazz percussion and drums, he said he loves music but now mostly plays piano and guitar.

Lux’s projected graduation is May 2020.

 

 

 


DAC Student Spotlight: Tianyi Li

Tianyi Li, DAC Ph.D. student in computer science

How do we form our opinions? How do we develop the mental models that make us different and unique?

Finding answers to these questions is what drives Tianyi Li’s research at the Discovery Analytics Center. As a Ph.D. student in computer science, her research interests include human-computer interaction (HCI), collective (crowdsourced) intelligence, visual analytics, and explainable artificial intelligence (AI).

“I have always been interested in human cognition and intelligence, especially the sensemaking process,” said Li, who is advised by Chris North at DAC and co-advised by Kurt Luther. “Studying computer science during my undergrad years at Hong Kong University made me think deeper about the relationship between human and computing intelligence. I am excited by how much computer science has been advancing our understanding of the black box of human intelligence by developing smarter and human-friendly technologies.”

Li’s thesis work is focused on how to modularize the complex sensemaking process so that many distributed agents can contribute to suitable components asynchronously and meaningfully. Specifically, Li explores, incites, acquires, and structures the wisdom of crowds to help make sense of the rapidly growing and dynamically changing information about the world. To achieve this goal, her work combines sensemaking theories, crowdsourcing techniques, and visual analytics tools to develop theories and applications for intelligence analysis.

“I like the great support I have in DAC,” said Li. “Many professors and students sit in the same area. Everybody is friendly, smart, and helpful. I am also benefiting a lot from my talented lab mates from diverse backgrounds and brilliant advisor Dr. North. The great lab atmosphere is really helping me grow into a more confident and mature researcher.”

Next month, Li will be at the ACM IUI 2019 conference in Los Angeles to present “What Data Should I Protect? Recommender and Planning Support for Data Security Analysts.” Last November, she presented the paper, CrowdIA: Solving Mysteries with Crowdsourced Sensemaking,” at the 21st ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW).

During an internship at Cloudera in Palo Alto, California, last summer, Li conducted interviews with data science practitioners in industry to collect user requirements and identify opportunities for leveraging interactive visual support and developed a prototype system called HyperTuner that supports hyper parameter search and analysis via interactive visual analytics.

In the summer of 2017, Li interned at Informatica in Redwood City, California. Her work there involved identifying what data is worth protecting and building an impactful plan to protect it. “I implemented a system prototype through four iterative design and evaluation cycles by applying user-centered design to this new domain of data security applications,” she said.

Li’s projected graduation date is Spring 2020. She said that based on her experience so far, she likes working both in academia and in industry. “Right now, I am just trying to work hard to keep both doors open until I finally decide which one to commit to,” she said.


IS-GEO announces Anuj Karpatne as 2019 inaugural Fellow

Anuj Karpatne, DAC faculty member and assistant professor of computer science

Anuj Karpatne, an assistant professor of computer science and a Discovery Analytics Center faculty member, has been named the 2019 Intelligent Systems and Geoscience (IS-GEO) inaugural Fellow.

The announcement was made by Suzanne Pierce, a research scientist at the Texas Advanced Computing Center (TACC) and principal investigator for the IS-GEO Research Coordination Network, during the American Association for the Advancement of Science (AAAS) conference in Washington, D.C., today.

“I am pleased to announce the IS-GEO Fellows Program,” Pierce said. “The program is designed to support researchers as they commit to in-depth projects to accelerate discoveries. Dr. Karpatne was selected because of his expertise in scientific theory{or physics}-guided machine learning. Throughout his fellowship year, he will evaluate applied machine learning approaches to Earth datasets for the energy industry.”

“We are at a crossroads of data-driven discovery in a number of scientific disciplines, such as earth sciences, that are witnessing a deluge of data and increased acceptance of data-driven, AI methodologies. However, to fully capitalize the promise of AI in accelerating scientific discovery, what is a needed is a paradigm shift that goes beyond current standards of ‘black-box’ AI research and embraces a deep synergy between scientific theories and AI, termed as theory-guided machine learning,” Karpatne said.

“Through the IS-GEO Fellows Program, I aim to expand the horizons of theory-guided machine learning, build new collaborations in the IS-GEO community, and solve impactful problems in the energy industry by using physics and data,” he said.

IS-GEO Fellow awardees are selected from the active membership of the IS-GEO community and receive an honorarium to explore new research areas with direct domain and real-world applications. Selected Fellows are encouraged to combine theoretical and scientific knowledge with applications to data problems from earth and environmental topics.

The AAAS Annual Meeting is the most widely reported global science gathering and the premier event to network with future collaborators across disciplines.


DAC Student Spotlight: Bijaya Adhikar

Bijaya Adhikari, DAC Ph.D. student in computer science

Bijaya Adhikari, a Ph.D. student in computer science, was attracted to the Discovery Analytics Center by the opportunity to solve data mining problems that are not only theoretically interesting, but have real-world applications, as well.

Adhikari’s core research focuses on graph mining and topics relating to social network analysis, such as community detection, immunization, influence maximization, and information. His interests also lie in machine learning, theoretical computer science, and algorithms.

Even as an undergraduate, the idea of developing methods for discovering non-obvious, non-trivial, and useful information and knowledge from seemingly arbitrary heap of massive data appealed to him.

“Seemingly unrelated processes like contagious diseases (e.g. flu and ebola) spreading over a population, inaccurate news articles and rumors dissemination over prevalent social networks, and word-of-mouth discussion can all be modeled as propagation over networks,” Adhikari said.

“We know that network structure plays a vital role in facilitating — or inhibiting — these processes,” he said. “So, we can solve many critical problems in the real world from various domains by leveraging graph mining techniques. Both in my past and current research, I have developed data mining tools for a succinct propagation-oriented network.”

Adhikari, who holds a bachelor’s degree in computer engineering from Vistula University in Warsaw, Poland, said that the “prospect of teaming up “with world-class researchers” also led him to DAC. Collaborative research with his advisor, B.Aditya Prakash, other faculty, and fellow Ph.D. students, has been presented at leading conferences and published in journal publications, including SIAM International Conference on Data Mining (SDM); The Web Conference (WWW); Pacific-Asia Conference on Knowledge Discovery and Data Mining; (PAKDD); and IEEE Transactions on Knowledge and Data Engineering (TKDE). (Links to the papers, slides, and codes can be found here.)

Most recently, Adhikari presented “NetGist: Learning to generate task-based network summaries” at the IEEE International Conference on Data Mining (ICDM 2018) in Singapore this past November.

While a DAC student, he gained real-world experience interning at WalmartLabs in Sunnyvale, California. Adhikari was part of the Search and Relevance team which focused on mining relations between queries based on customer’s engagement data. He hopes to graduate in Spring 2020 and continue working in the data mining field.

 

 

 


Grad students say that UrbComp offered valuable cross discipline skills for solving urban problems

Fanglan Chen (left), Mohammed Almannaa (middle), and Swapna Thorve (right)

Current Virginia Tech graduate students Mohammed Almannaa, Fanglan Chen, and Swapna Thorve have earned the Urban Computing (UrbComp) certificate, a cross disciplinary program sponsored by the National Science Foundation and led by the Discovery Analytics Center.

The program is a collaboration between eight departments and five colleges and trains students to use both foundational and applied aspects of data science to help solve problems related to urban issues like transportation, affordable housing, and policing.

Almannaa holds a master’s degree in civil engineering from Virginia Tech and, advised by Hesham Rakha, is currently pursuing a Ph.D. in the same major.

Among his research interests are bike sharing systems, eco-driving, highway transportation safety, and intelligent transportation systems.

“I was fortunate to enroll in the UrbComp program during the first semester of my Ph.D. journey. It was an invaluable experience, giving me the ability to think out of the box and come up with innovative approaches to solving complex problems,” said Almannaa.

He specifically cites the core course, Urban Computing, for “motivating and pushing me substantially.”

“It gave me the chance to work on a group project with people in different disciplines, providing an appreciable opportunity to look at issues and problems from different perspectives,” said Almannaa.

Chen is a Ph.D. computer science major at the Discovery Analytics Center, advised by Chang-Tien Lu, and working toward a simultaneous doctoral degree in urban planning. Her research focuses on both housing and transportation.

Chen said she decided on the UrbComp program because she wanted to explore how new data sources and methods might be usefully applied to the challenging issues faced by today’s urban planners.

“For example, while creating more attractive and pedestrian-friendly cities, we also aim to provide affordable housing and a convenient transportation system,” said Chen, whose research focuses on both housing and transportation. “A growing network of sensors, wireless devices, and data centers of the key infrastructure make big data easier to gather and analyze to help address urban issues like this more effectively.”

She said that UrbComp also exposed her to a large range of research topics, including energy analytics, epidemiology computing, and social media mining and to the ethical implications and consequences of algorithmic decisions.

For Swapna Thorve, it was the variety and freedom of courses that drew her to the UrbComp program.  A Ph.D. student in computer science, Thorve said the program offers a different and unique skill set that computer scientists should develop.

“Converting real world problems to computer science problems and understanding the different perspectives of students from various backgrounds are two important things I have learned from this program,”  said Thorve, who is a graduate research assistant in the Network Dynamics and Simulation Science Laboratory and advised by Madhav Marathe.

Her research interests include developing machine learning models, simulations, and smart grid analytical platforms. UrbComp, Thorve said, has provided her the opportunity to collaborate on an array of research problems with students in other departments and disciplines.

(Gloria Kang and Huthaifa Ashqar were the first two Ph.D. students to graduate from Virginia Tech with an Urban Computing program certificate. Read what they have to say about the program here.)

For more information about the Urban Computing certificate, contact Wanawsha Shalaby, program coordinator.

 

 

 


DAC faculty and students attend AAAI-19 conference to discuss their research

You Lu (left) and Chidubem Arachie (right), both DAC Ph.D. student in computer science, presenting their posters.

Discovery Analytics Center faculty Bert Huang and Chandan Reddy and two Ph.D. students were in Honolulu, Hawaii, last week, sharing their research with attendees at the Thirty-Third AAAI Conference on Artificial Intelligence.

Chidubem Arachie and You Lu, both in the Department of Computer Science, presented spotlight talks on studies they collaborated on with Huang, who is their advisor. The studies were also included in a poster session.

Arachie’s presentation was on Adversarial Label Learning (ALL), a method they introduced to train robust classifiers when access to labeled training data is limited. ALL trains a model without labeled data by making use of weak supervision to minimize the error rate for adversarial labels, which are subject to constraints defined by the weak supervision. Their study demonstrated that their method is robust against weak supervision signals that make dependent errors. Their experiments confirm that ALL is able to learn models that outperform the weak supervision and baseline models. ALL is also capable of directly training classifiers to mimic the weak supervision.

Lu presented Block Belief Propagation for Parameter Learning in Markov Random Fields. In this paper, the researchers developed block belief propagation learning (BBPL) for training MRF and theoretically proved that BBPL has a linear convergence rate and that it converges to the same optimum as convex BP. Their experiments show that, since BBPL has much lower iteration complexity, it converges faster than other methods that run truncated or complete inference on the full MRF each learning iteration.

Reddy gave an invited talk at the AAAI conference workshop on health intelligence, where he discussed ways to create natural language interfaces for both clinicians and patients and enable them to ask some basic questions on medical knowledge bases and clinical databases. This work, he said, will efficiently and effectively provide answers to questions and results to queries in the biomedical domain.


DAC Student Spotlight: Joseph Weissman

Joseph Weissman, DAC master’s student in statistics

Joseph Weissman graduated from Virginia Tech in May 2018 with triple majors — mathematics, physics, and Computational Modeling and Data Analytics (CMDA).

“I really gravitated toward the machine learning side of my CMDA classes,” Weissman said. “And because I wanted to learn more about networks, I took Dr. Sengupta’s class on the subject during my last semester.”

His final project for the class evolved into the research project he is now working on as a master’s student at the Discovery Analytics Center, where he is advised by Srijan Sengupta.

“Often real world networks have densely connected core nodes and sparsely connected periphery nodes. We developed a hypothesis test to determine if a network exhibits this core-periphery structure,” Weissman said. “A network has core-periphery structure if it can be broken into a set of core nodes which are highly connected and periphery nodes which are sparsely connected.”

Since this method works for practically any network, he said, there are many applications. “One example we have been playing with is finding the core members of a social group,” Weisman said.

Last week at the Scientific Machine Learning conference at Brown University in Providence, Rhode Island, Weissman presented the work he has done Sengupta, “Core-Periphery Inference,” during a poster session for graduate students.

During the fall semester, Weissman was also busy helping Mark Embree, DAC faculty and professor of mathematics, design data science classes for the Virginia Tech Honors College Calhoun Discovery Program.

“I love working on challenging research projects with real world applications,” said Weissman, who held two internships last summer. As a data analyst intern for Capital One, he helped migrate data products to Amazon Web Services and, as a data analyst intern for a defense company, he worked on helping to solve deep learning problems.

Weissman expects to earn a master’s degree in mathematics in Spring 2019. Currently, he said, he is leaning toward working for a year before pursuing a Ph.D. in statistics.


DAC Student Spotlight: MD Momen Bhuiyan

MD Momen Bhuiyan, DAC Ph.D. student in computer science

MD Momen Bhuiyan, a Ph.D. student in computer science at the Discovery Analytics Center, is focusing his research on social computing. He is currently working on news consumption issues in relation to social media, trying to solve problems of fake news through computation and design.

“The problem of fake news is endemic in our social feeds,” Bhuiyan said. “As a solution, I am using design as a way of helping users identify problematic information sources.”

In November, he presented a poster, “Feed Reflect: A Tool for Nudging Users to Assess News Credibility on Twitter,” at the 2018 ACM Conference on Computer-Supported Cooperative Work and Social Computing in New York City. Collaborators included his advisor Tanushree Mitra.

Bhuiyan holds a bachelor’s degree in computer science from the Bangladesh University of Engineering and Technology. He was attracted to the Department of Computer Science at Virginia Tech because of its diverse faculty and great reputation in Human Computer Interaction (HCI).

As an undergraduate, he was primarily interested in the application of computing tools and most of his exposure was to data mining and natural language processing.  “My undergraduate experience helped me build a repertoire for their applications in graduate school,” he said.

“My research at DAC has allowed me to connect with a students in other fields who work on interconnected problems,” Bhuiyan said. “This opportunity is valuable in doing graduate work.”

Bhuiyan, on track to graduate in Spring of 2020, said he would like to apply his skills in an industry position where he could make a meaningful contribution.


Professor will use new machine learning techniques to decrease deaths resulting from traumatic brain injury

Chandan Reddy (left) is an associate professor in the Department of Computer Science and faculty at the Discovery Analytics Center.

To help physicians decrease the number of deaths resulting from traumatic brain injuries, Chandan Reddy, associate professor in the Department of Computer Science and faculty at the Discovery Analytics Center,  will use new machine learning techniques for computational models to predict short- and long-term outcomes, categorize traumatic brain injury patients, and provide interventions tailored to a specific patient and his or her injury. This four-year study is funded by a National Science Foundation grant in excess of $1 million. Click here too read more about the grant.


Two Ph.D.s first graduates of NSF-sponsored urban computing program

Gloria Kang (left) and Huthaifa Ashqar (right)

Gloria Kang and Huthaifa Ashqar recently earned doctorates from Virginia Tech in totally different fields, but they have something in common — cross-disciplinary training to solve today’s tough urban challenges.

Kang and Ashqar are the first graduates of the National Science Foundation-sponsored urban computing certificate program. Both are planning to walk at the December commencement ceremony in Blacksburg.

Administered through the Discovery Analytics Center, the program trains students across disciplines in the latest methods in analyzing massive datasets to study key issues concerning urban populations.  Click here to read more about Gloria and Huthaifa.


Naren Ramakrishnan reappointed Thomas L. Phillips Professor of Engineering

Naren Ramakrishnan, professor of computer science and Director of DAC

Naren Ramakrishnan, professor of computer science in the College of Engineering at Virginia Tech and director of the Discovery Analytics Center, was reappointed as the Thomas L. Phillips Professor of Engineering by Virginia Tech President Tim Sands and Interim Executive Vice President and Provost Cyril Clarke. Click here to read more about Naren’s reappointment.


DAC Student Spotlight: Shruti Phadke

Shruti Phadke, DAC Ph.D. student in the Department of Computer Science

The relevance of Tanushree Mitra’s research and its socio-psychological aspect attracted Shruti Phadke to the Discovery Analytics Center in Fall 2017 while she was earning a master’s degree in computer science at Virginia Tech.

“I have always been inclined to work in interdisciplinary fields so the opportunity to work with Dr. Mitra seemed like a perfect fit for me,” said Phadke.

Phadke studies the social influence of hate groups and conspiracy communities on social media. At present, she is analyzing language used by hate groups (like neo-Nazis, anti-LGBT, anti-Muslim, and white supremacists) on Twitter representing ideologies that affect democratic values of society. She is also studying communities like Reddit Conspiracy (r/conspiracy) that cause panic and spread mistrust in organizations.

“From looking at even just a few of the messages posted by hate groups and conspirators, it is easy to understand how manipulative and influential such communication can be to their targeted audience,” said Phadke.

Currently, natural language processing (NLP) and network analysis tools are not sophisticated enough to understand such complex context of hate and propaganda, she said.

“We have to take inspiration from sociology and psychology regarding community behavior, propaganda, and influence. The multidisciplinary nature of the research I am doing attracts me the most,” Phadke said.

“And being part of a huge DAC community has been great,” she said. “Through events hosted by center, I have had a chance to meet faculty from all around the campus.”

Phadke is at the 2018 ACM Conference on Computer-Supported Cooperative Work and Social Computing in New York City this week to present the poster, Framing Hate with Hate Frames: Designing the Codebook. In their research, Phadke and her coauthors — who include Mitra and James Lawdon, a Virginia Tech sociology professor — offer a two-fold outlook on hateful social media communications. First, they adopt a collective action perspective to analyze how hate groups identify problems in the social groups they target, suggest solutions to the problems, and motivate their supporters. Then, the researchers develop a codebook highlighting strategies of influence through the lens of propaganda devices.

Phadke is also serving as a student volunteer for the conference.

She is projected to receive her Ph.D. in 2022. “Ideally, I would like to work for a journalism organization that computationally studies policies and deviance in an online world,” Phadke said.


Virginia Tech study identifies recurring elements in conspiracy theories to learn what people who propagate them are thinking

Tanushree Mitra, DAC faculty member and assistant professor of computer science

What do online conspiracy theorists discuss; what are the recurring elements in these conversations; and what do they tell us about the way people think?

As Tanushree Mitra, assistant professor of computer science and a faculty member at the Discovery Analytics Center, and Mattia Samory, a post doc in the Department of Computer Science, set out to find answers, they turned to Reddit, a social media platform of thousands of smaller communities or “subreddits” connecting users with similar interests. Click here to read more about Tanu’s research.


DAC Student Spotlight: Khoa Doan

Khoa Doan, DAC Ph.D. student in computer science

After graduating with a bachelor’s degree in computer science from Webster University, Khoa Doan entered the workforce. For the next few years, he held positions as a software developer and data engineer in the advertising industry and at NASA and gained experience processing large datasets.

“I came to appreciate both the theoretical and practical contributions,” said Doan. “Working with large datasets is tricky because solutions become much more constrained. The challenge is what interests me and it makes me really happy if I am able to solve a problem.”

Doan decided to pursue a master’s degree in computer science at the University of Maryland. When looking for a Ph.D. program, he was attracted to Virginia Tech and the Discovery Analytics Center “because of a good mix of strong theoretical foundation and practical research objectives. There is a diversity and plethora of research opportunities, especially in things that matter.”

And since being at DAC, he said, “I have learned a lot from the DAC community. I have good friends with both similar and diverse research interests.”

Doan’s main research interest is in scalable machine learning and data mining. His current focus is on deep hashing for similarity search, using neural networks as a basis for efficiently “searching” for similar items in very large databases. For example, he searches for similar documents in news articles, books or papers, and images.

“This problem is very hard because we have to pay attention to both efficiency — how to retrieve the items fast, and sometimes in real-time — and effectiveness,” said Doan. “Items can be similar because of similar words, but also because of similar authors, or similar topics, thus it is very difficult to choose the right concept to describe similarity and convert these informal concepts into mathematical equations.” Doan is also working with his advisor, Chandan Reddy, on research with Criteo, a leading advertising company that has made a significant investment in machine learning.

“Having worked in the advertising industry, solving computational problems in this field is of interest to me, as well, and is a great opportunity,” Doan said.


DAC Student Spotlight: Zhiqian “Danny” Chen

Zhiqian “Danny” Chen, DAC Ph.D. student in the Department of Computer Science

Generating new music inspired by existing music datasets is a major area of interest for Zhiqian “Danny” Chen, a Ph.D. student at the Discovery Analytics Center.

“Music, with its complex hierarchical and sequential structure and its inherent emotional and aesthetic subjectivity, is an intriguing research subject at the core of human creativity,” said Chen. “And because of rapid advances in data-driven algorithms such as deep learning, exploring computational creativity via machine learning approaches is increasingly popular.”

While this exploration has included some work on generative models for music, research that investigates the capabilities of deep learning for creative applications such as style transfer on images and video is limited, he said.

The paper, “Learning to Fuse Music Genres with Generative Adversarial Dual Learning,” aims to fill this space by exploring the idea of style fusion in music with generative adversarial dual learning. Chen presented the paper at the 2017 IEEE International Conference on Data Mining (ICDM) in New Orleans, Louisiana.

In November, Chen will attend ICDM 2018 in Singapore, where he will present “Rational Neural Networks for Approximating Jump Discontinuities of the Graph Convolution Operator,” a study on deep graph learning.

“Effective information analysis generally boils down to the geometry of the data represented by a graph,” said Chen. Typical applications include social networks, transportation networks, spread of epidemic diseases, neuronal networks, biological regulatory networks, telecommunication networks, and knowledge graphs, defined over non-Euclidean graph domains.

Chen’s research in this area focuses on modeling graphs in spectral domains for deriving representations for node level embeddings. He uses graph notions such as adjacency matrices or graph Laplacians to describe geometric structures and reveal latent patterns.

He holds a bachelor’s degree in software engineering and Japanese from Huazhong University of Science and Technology, China, and a master’s degree in software engineering from Peking University, China.

Advised by Chang-Tien Lu, Chen is projected to graduate with a Ph.D. in computer science in Fall 2019.


DAC welcomes new faculty Jiepu Jiang and Anuj Karpatne

Anuj Karpatne (left) and Jiepu Jiang (right), DAC faculty members and assistant professors in the Department of Computer Science

The Discovery Analytics Center continually brings together computer scientists, engineers, and statisticians to meet the research and workforce needs of today’s data-driven society. This fall, DAC welcomes two new faculty to bolster its strengths in information retrieval, data mining, human-computer interaction, and information science.

The two new faculty members are Jiepu Jiang and Anuj Karpatne, both assistant professors in the Department of Computer Science.

Jiepu Jiang joins Virginia Tech from the University of Massachusetts Amherst, where he worked with James Allan on researching information retrieval techniques at the Center for Intelligent Information Retrieval. He also taught a graduate level course on information retrieval.

In 2016, he earned a Ph.D. in library and information science from the University of Pittsburgh. His dissertation was entitled “Ephemeral Relevance and User Activities in a Search Session.

Presently, Jiang is working toward another doctoral degree in computer science from the University of Massachusetts.

Jiang said he is committed to helping people quickly find and use information. His current research agenda is to study sociotechnical issues between human and various AI systems, particularly search engines, conversational systems, and exploratory text analytics systems. He is also teaching a graduate course on Information Storage and Retrieval at Virginia Tech this fall.

Jiang has been regularly published in leading information retrieval and data mining conferences such as SIGIR, WSDM, and CIKM. In 2017, he received the best student paper award from the ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR) for his work on understanding dynamics of search result judgments in information retrieval.

“I feel greatly fortunate to join DAC, a highly vibrant, diverse, and interdisciplinary group working on cutting-edge data analytics problems,” Jiang said.

Anuj Karpatne received his Ph.D. from the University of Minnesota with Vipin Kumar in September 2017. Following graduation, he was a postdoc with Kumar until joining Virginia Tech in August 2018.

His research explores how data mining and machine learning methods can accelerate scientific discovery and address some of the major challenges facing our society. A primary focus of Karpatne’s research is to advance the paradigm of theory-guided data science, where machine learning methods are deeply integrated with scientific knowledge (or theories) that underlie real-world phenomena in physical and life sciences. An overarching goal of this paradigm is to develop generalizable and physically consistent machine learning methods that can augment current gaps in our understanding of physical processes by effectively using physics and data. Karpatne’s prior research builds the foundations of this paradigm and explores its applications at the intersection of food, energy, and water.

He is teaching an advanced topics course on Machine Learning Meets Physics this semester, which is aligned with his research interests.

Karpatne said he is excited to be a part of DAC to work on inter-disciplinary problems at the intersection of data science and scientific problems. “DAC provides an ideal setup to fully explore the power of data science methods in accelerating scientific discovery,” said Karpatne. He is looking forward to collaborate with DAC students and researchers who are interested in solving real-world problems in physical and life sciences by pursuing novel research in data science.

Karpatne is also a coauthor of the textbook “Introduction to Data Mining (2nd edition),” published by Pearson.

“Virginia Tech is leading the way in big data research and education,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science and DAC director. “Adding faculty like Jiepu and Anuj, experts in their respective fields, not only enhances DAC’s research capabilities but offers tremendous educational opportunities to our students as they are exposed to cross-cutting areas.”

The Discovery Analytics Center has become a well-recognized force among the analytics community within the commonwealth and beyond, and fosters multi-stakeholder collaborations with fellow universities, leading industry affiliates, government agencies, and nonprofit organizations. Officially housed within the Computer Science Department, faculty and graduate students represent computer science, statistics, electrical and computer engineering, and math.


DAC Student Spotlight: Chen Gao

Chen Gao, DAC Ph.D. student in electrical and computer engineering

Chen Gao traveled to Newcastle, United Kingdom, last month to present a paper on human-object interaction at the 29th British Machine Vision Conference, a major international conference on computer vision and related areas held in the UK.

Gao is a first-year Ph.D. student in the Bradley Department of Electrical and Computer Engineering. After graduating with a master’s degree in electrical and computer engineering from the University of Michigan Ann Arbor in April 2017, Gao came to Virginia Tech as a visiting research assistant to work with Jia-Bin Huang. It was this experience that sparked his interest in the university’s Ph.D. program. Huang, a faculty member at the Discovery Analytics Center, is now his advisor.

“Being a DAC student in the era of big data, I really appreciate that we can benefit from access to massive datasets and develop algorithms to learn patterns,” said Gao, whose career goal is to work in research and development in a company like Facebook or Google.

Gao’s passion for computer vision began with a TED talk by Stanford University Professor Feifei Li. “She spoke about giving sight to machines, teaching them to see and then helping us see better,” he said. “After listening to her, I kept asking myself: ‘How could we teach machines to be more intelligent and, in return, how could computer vision benefit our daily life and the natural environment to create a better future?’”

This question, Gao said, is the motivation behind his “challenging, but very practical” research at DAC. His focus is on human-object interaction detection, a crucial step toward a finer-grained understanding of an image.

“Given an image, we not only detect all the objects in the image, but also detect all the interactions between human and objects. Millions of images are uploaded to social media every day, thus it is essential to cluster images according to the content. Our research provides a potential solution to automatically cluster images according to actions,” he said.

Gao has submitted the paper, Panoramic Robust PCA for Foreground-Background Separation on Noisy, Free-Motion Camera Video, to the IEEE Transactions on Computational Imaging for review. This paper is a journal extension of Augmented Robust PCA for Foreground-Background Separation on Noisy, Moving Camera Video at the 2017 IEEE Global Conference on Signal and Information.

His projected graduation date is June 2022

 

 


DAC faculty and students share research, organize workshop at 2018 IEEE VIS Conference in Berlin

Chris North, associate director of the Discovery Analytics Center, and Ph.D. students Michelle Dowling and John Wenskovitch will be in Berlin, Germany, from Oct. 21 to 26, attending the 2018 IEEE VIS Conference.

In addition to presenting their research, the three are organizers of a conference workshop: Machine Learning from User Interaction for Visualization and Analytics.

IEEE VIS is the worldwide largest and most important conference on Scientific Visualization, Information Visualization and Visual Analytics. It is the premier forum for advances in visualization in academia, science, government, industry, and beyond.

Dowling, who is also a National Science Foundation research trainee in the Urban Computing Certificate program, will present SIRIUS: Dual, Symmetric, Interactive Dimension Reductions, which she coauthored with Wenskovitch, DAC Ph.D. student J.T. Fry, and DAC faculty Leanna House, Scotland Leman, and North.

Wenskovitch will present the second accepted DAC paper, The Effect of Semantic Interaction on Foraging in Text Analysis, which he coauthored with DAC Ph.D. student Lauren Bradel, Dowling, House, and North.

The workshop taking place on Oct. 22 has been designed to bring together researchers from across all VIS fields to share their expertise and generate an open discussion about what is currently learned from user interaction and where future research in this area can go.


DAC Student Spotlight: Rongrong Tao

Rongrong Tao, DAC Ph.D. student in computer science

The prospect of being located in the heart of Northern Virginia drew Rongrong Tao to Virginia Tech and the Discovery Analytics Center in the National Capital Region. A Ph.D. student who earned a master’s degree in computer science at the University of Michigan in Ann Arbor, Tao conducts research on misinformation detection and analysis.

“This location provides opportunities for collaboration with academic institutes in the metropolitan D.C. area and an advantage for future career advancement,” said Tao. After graduation, she would like to work in an industry where she can apply data mining techniques to real-world problems.

Tao said she particularly likes the variety of projects and different type of data available to students at DAC. “While working on our own projects, we can also collaborate with fellow students on other interesting projects,” she said.

Her most recent research is on fake news detection. “Compared to existing approaches which mostly treat fake news detection as a binary classification problem, we propose an approach to locate the pieces of misinformation out of the news stories,” Tao said.

Tao said that with so much information coming from so many sources it has become increasingly more difficult to tell whether the information is actually incomplete or manipulated on purpose. “Although domain experts may help clarify misinformation, it requires lots of labor and is hard to catch up with the rapid growth of information,” Tao said. “Hence, we are interested in automating misinformation detection by studying the mechanism of misinformation propagation.”

Previously she worked on an unsupervised approach to detect media self-censorship using social media as a sensor. In that research, the proposed framework is evaluated over six Latin American countries and can be used to provide an indicator of broader media freedom.

Tao coauthored STAPLE: Spatio-Temporal Precursor Learning for Event Forecasting published in the Proceedings of the 2018 Society for Industrial and Applied Mathematics (SIAM) International Conference on Data Mining.

Tao is projected to graduate in 2019.  Her advisor is Naren Ramakrishnan.


DAC Student Spotlight: Ellis Kessler

Ellis Kessler, UrbComp Ph.D. student in Math

Ellis Kessler graduated from Virginia Tech in the spring of 2017 with a bachelor’s degree in mechanical engineering. The following fall, he was back at the university working toward a Ph.D.

“As an undergraduate, I knew I wanted to do some kind of research with vibrations or structural dynamics. My adviser, Pablo Tarazaga, taught about vibrations and by working with him in the Virginia Tech Smart Infrastructure Laboratory, I became involved with vibrations research on the Goodwin Hall building,” Kessler said.

“Goodwin has 225 high sensitivity accelerometers permanently mounted to the building’s structure. Because there so many sensors available there, the amount of data we can collect is very large,” said Keller, “and this led me, in a natural progression, to the Discovery Analytics Center.

Kessler’s main research as a DAC Ph.D. student remains with human-structure interaction in the Goodwin building. In his research, Kessler looks for answers to questions like these: When someone walks along a hallway, can we tell exactly where that person is and can we tell something about that person just from measuring the vibration of the floor he or she is walking on? For instance, can we tell whether the person is male or female? Can we distinguish between two individuals just based on the floor acceleration measurements? Or can we tell something about that person’s health from their gait measurements?

“I think the most interesting thing about being at DAC is the ability to work with large and complex data sets which are becoming more and more prevalent in research,” Kessler said. “It is rewarding to be pushing the boundaries of research in this way.”

His DAC faculty advisor is Mark Embree, associate director of the Virginia Tech Smart Infrastructure Laboratory.

Kessler is also a National Science Foundation research trainee in the UrbComp program administered through DAC.

This past summer, Kessler was a graduate supervisor for a National Science Foundation-funded Research Experiences for Undergraduates (REU) site in Darmstadt, Germany. While at the Technical University in Darmstadt (TUD), he collaborated with other doctoral students and they are currently in the process of submitting a paper on their research to a conference next spring in Copenhagen.

After graduation, projected for May 2019, Kessler would like to work at a national research lab.


DAC and UrbComp students garner Deloitte Foundation Data Analytics Fellowship to fund their research

Ph.D students Jonathan Baker (left), Sirui Yao (middle) and Leanna Ireland (right).

Jonathan Baker and Sirui Yao, Ph.D. students at the Discovery Analytics Center, and Leanna Ireland, a National Science Foundation research trainee in the Urban Computing (UrbComp) Certificate program administered through DAC, have each been awarded a Deloitte Foundation Data Analytics Fellowship in the amount of $10,000 to fund their research.

Baker, also a National Science Foundation research trainee in the UrbComp program, is a math major advised by Mark Embree, professor of mathematics, associate director of the Virginia Tech Smart Infrastructure Laboratory, and DAC faculty.

Yao is a computer science major advised by Bert Huang, assistant professor of computer science and DAC faculty.

Leanna Ireland, a sociology major, is advised by James Hawdon, professor and director of the Center for Peace Studies.

The three are among five graduate students — selected from applications received from across five colleges at Virginia Tech — to receive this interdisciplinary fellowship in support of the university’s Data and Decisions Destination Area vision.

A committee consisting of four members of Data and Decisions and three representatives from Deloitte chose the fellowship recipients for 2018-2019.

Baker’s project was motivated by disasters like the 1995 collapse of a large department store in Seoul, South Korea, which killed 500 people and injured 1400. In spite of the fact that a few hours before the collapse, occupants began to feel vibrations from the air conditioning system throughout the building, no evacuation was ordered.

A building equipped with vibration sensors and software could prevent such a disaster in several ways. First, by monitoring the global vibrations of the building, software should be able to automatically detect even small amounts of structure damage so that repairs can be conducted long before evacuation becomes necessary. Second, once vibrations indicate that they building is in danger of collapse, the system could trigger an alarm, just as smoke detectors may automatically signal evacuation. Lastly, the building could use vibrations to help occupants respond intelligently to an ongoing evacuation in response to any emergency. Foot-traffic vibrations can also be used to estimate the locations of occupants and calculate real-time evacuation routes that minimize crowding, help prevent stampeding, and ensure that the building is emptied as quickly and safely as possible. The building may also be able to detect circumstances that would make some exits unavailable and adapt its evacuation directions accordingly.

By triggering the alarm and giving evacuation instructions, a smart building takes the role of emergency personnel: the building itself is the first responder. The goal of this project is to develop algorithms that this kind of intelligent building would require.

Yao’s project is focused on recommender systems.

Recommender systems play an important role in supporting human decision making. However, it is important to be aware of the potential impact of applying such technology, especially to areas that involves humans. Fairness is a crucial aspect to be taken into account. Since recommender systems are trained on data collected from the real world, which already has a long history of human bias, such data can be severely contaminated and historical biases passed on or reinforced through recommender models. It is unethical to make recommendations that constantly favor one group over the others. More concretely, unfair treatment of users would cause poor user experiences and could lead to legal trouble.

Yao’s research proposes to establish methods for measuring, analyzing, and mitigating unfairness in recommender systems. The goals are threefold: (1) to quantify and evaluate unfairness; (2) to identify the causes of unfairness; (3) to promote fairness. The success of this research will have significant impact on the wide-reaching technology of recommender systems and the many aspects of society they affect.

Ireland’s research involves crime-fighting and crime-control mobile and web applications that the general public can, for example, use to submit tips and/or share photos directly to the police.

Official crime statistics are often patchy and can be plagued by missing data, biased reporting and other measurement aliments. Crowdsourcing data can account for some of these limitations in official and self-reported crime data sources, such as lagged, incomplete, or often skewed data. However, there is also some apprehension that crowdsourced data-sources could include false-reports, trolls, and the misidentification of offenders. Relatedly, minority voices could be under-represented.

To address the potential differences in crowdsourced-policing and official policing initiatives, Ireland will investigate how the crowd-sourced initiative called the French Quarter Task Force (FQTF), colloquially known as the “Uber for cops,” impacts official crime reports. And, does success of the FQTF lead to greater community engagement, and if so, how, if at all, does the FQTF affect biased reporting? With advantages and disadvantages in both types of data, drawing from both formal and crowd-sourced data could present a clearer picture of the occurrence of crime in society, suggesting the need to include all data sources in criminological research.

The other two Virginia Tech students receiving the Deloitte Fellowship are Kaveh Kelarestaghi, a civil engineering major, and Long Xia, a business information technology major.

“A special thank you to Deloitte for initiating this interdisciplinary fellowship for our graduate students and for supporting the Data and Decisions Destination Area vision,” said Robin Russell, a member of the Data and Decisions Stakeholders Committee and chair of the Deloitte Foundation Data Analytics Fellowship Selection Committee, in a letter announcing the recipients. “We look forward to seeing the results of the research projects and engaging with these talented students.”

The Data and Decisions Destination Area seeks to advance the human condition and society with better decisions through data and to be a global destination for data analytics and decision sciences, integrating across all Destination Areas and Strategic Growth Areas of the university.


DAC Student Spotlight: Kaiqun Fu

Kaiqun Fu, DAC Ph.D. student in computer science

In his research, Kaiqun Fu uses spatial data mining, urban computing, and machine learning to infer crime rates/types from street view images, roadway networks, and criminal records. Applying deep learning methods also uncover hidden safety-related patterns from the physical appearance of street blocks that help address urban safety issues.

“My original intent was to improve on previous work done on crime type classification problems with spatiotemporal data such as criminal records and roadway networks,” said Fu, a Ph.D. student at the Discovery Analytics Center advised by Chang-Tien Lu. “But when we were able to access street view images from one of our sponsors, the District Department of Transportation, we saw a potential opportunity to explore crime rates and type prediction from street view images, as well.”

Next month at the 2018 ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Fu will present a paper that he has coauthored about this research: “StreetNet: Preference Learning with Convolutional Neural Network on Urban Crime Perception.”

Fu has also worked with the District Department of Transportation (DDOT) on a research project applying social media analysis to intelligent transportation systems.

“My team and I developed a social media-based transportation status monitoring and situation summarization system for DDOT,” he said. “The proposed system monitors and retrieves transportation-related tweets and, based on the retrieved Twitter data, the system is capable of detecting traffic incidents and highlight traffic status with text summarization techniques.”

Fu has presented two coauthored papers on the research for DDOT: “Steds: Social Media Based Transportation Event Detection with Text Summarization,” at the IEEE International Conference on Intelligent Transportation Systems (ITS); and “Social media data analysis for traffic incident detection and management” at the Transportation Research Board (TRB) conference, both in 2015.

Fu holds a master’s degree in computer science from Virginia Tech and is projected to graduate with a Ph.D. in computer science in 2019.

“High influence in my area of research is mainly what attracted me to the university and the Discovery Analytics Center,” said Fu. “As such an active player in the data mining, machine learning, and urban computing research fields, DAC has provided me great opportunities for working with interdisciplinary corporations.”


DAC Student Spotlight: Sorour Ekhtiari Amiri

Sorour Ekhtiari Amiri, DAC Ph.D. student in computer science

Sorour Ekhtiari Amiri developed an interest in machine learning during her senior year of college. After earning a bachelor’s degree in computer engineering from Beheshti University, she worked on machine learning applications while getting a master’s in computer engineering at the University of Tehran.

Amiri then decided to pursue a Ph.D. in computer science.

“I chose Virginia Tech and the Discovery Analytics Center,” Amiri said, “because of the great opportunity to collaborate with high impact researchers in the areas of data mining and machine learning.”

Amiri’s research is focused on summarizing large graphs and graph sequences based on a given task. She targets the task-based graph summarization problem, looks at various types of graphs, and uses deterministic and learning based approaches to generate high-quality graph summaries.

“Large graphs — also referred to as network — appear everywhere, as they very well capture the relation between objects,” Amiri said.

“For example, social networks, co-purchased product networks, people contact networks, and protein interaction graphs are instances of large graphs in the real world. Analyzing these graphs and solving various tasks on them has many applications in different fields such as cybersecurity, recommendation systems, sociology, and biology. However, the increasingly large size of such networks makes it challenging to visualize and analyze them, highlight their important characteristics, and perform fast computations on them,” said Amiri, whose DAC faculty advisor is B. Aditya Prakash.

Results of her research while a DAC student have been presented at a number of national and international conferences, including the IEEE International Conference on Data Mining series (ICDM); European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (PKDD); and the Association for the Advancement of Artificial Intelligence (AAAI) conference.

Her work has also appeared in journals such as the IEEE Transactions on Knowledge and Data Engineering (TDKE) and Data Mining and Knowledge Discovery (DAMI).

Amiri spent this past summer as an intern with Google’s “search ad” team, developing and training machine learning models and generating a new signal for using in search ads auction. She also analyzed machine learning models and other search ads signals.

Amiri expects to graduate in spring 2019


Ph.D. student Yuliang Zou presents DAC research at ECCV 2018

DAC Ph.D. student Yuliang Zou shares research on unsupervised learning of depth prediction and optical flow estimation at ECCV.

Yuliang Zou, a Ph.D. student at the Discovery Analytics Center, was in Munich, Germany, earlier this week to participate in the 2018 European Conference on Computer Vision ECCV.  The conference, held every other year, is one of the most influential academic conferences for this area of research.

At the main conference, Zou presented a poster on “DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency.”

This research, conducted with his DAC faculty advisor Jia-Bin Huang and Zelun Luo, a Stanford University student, focuses on unsupervised learning of depth prediction and optical flow estimation —  two fundamental problems in computer vision with many applications.

‘It is difficult to collect high-quality dense annotated data to train the models for such dense prediction tasks, so we propose an unsupervised method to train these models,” said Zou. “Our core idea is that motions for the static and non-occluded pixels can be fully determined by the depth values and the camera pose transformations. On the other hand, we have another network to estimate the motions simultaneously. As a result, we have two different ways to describe these motions. We can thus leverage the inconsistency between the two predictions as a supervisory signal to help train the model.”

Zou also presented “iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection,” at the 1st Person in Context (PIC) Workshop. The submission secured a third place on the person in context challenge and received a prize with two NVIDIA GPUs.

Four other papers co-authored by Huang were presented at the ECCV conference:


DAC Student Spotlight: Matthew Slifko

DAC faculty member Scotland Leman (left) and DAC Ph.D. student and UrbComp research trainee Matt Slifko discuss spatial relationships in the housing market.

It is no coincidence that Matthew Slifko’s research in predictive modeling in the presence of big and/or messy data deals specifically with the real estate market.

“As I prepared to return to grad school in July 2013, I was selling the house that I had bought at the beginning of the housing bubble in 2008,” said Slifko. “While predictive modeling had always been a favorite topic of mine as a student, real estate was a personal interest before it became an academic one.”

As a DAC Ph.D. student majoring in statistics, he was able to combine the two into a perfect fit.

Slifko said the type of data he works with can be problematic for a number of reasons. “For example, the size of the data presents computational challenges. And, incorrect data  — such as a 100 square foot house with three bedrooms — interferes with the ability to build predictive models,” said Slifko, whose advisor is Scotland Leman.

“My research focuses on methods for using information about properties, real estate transactions, and market events like a natural disaster or a housing bubble to understand the behavior of property values in the presence of messy data,” he said.

Being a DAC student has given him the opportunity to collaborate with people from disciplines outside his own. “Learning how other disciplines view problems and how to communicate with non-statisticians is invaluable,” said Slifko, who is also a National Science Foundation research trainee in the Urban Computing (UrbComp) Certificate program, administered through DAC.

Earlier this year he was part of an UrbComp team that took second place during the final round of the  2018 Data Ethics Case Competition sponsored by the Center for Business Intelligence & Analytics.

Slifko earned a bachelor’s degree from the University of Pittsburgh at Johnstown and a master’s degree from Indiana University of Pennsylvania, both in math. His projected graduation date from Virginia Tech is Summer 2019, after which he hopes to secure an academic position.


DAC Student Spotlight: Chidubem Arachie

Chidubem Arachie, DAC Ph.D. student in computer science

As a computer science student at the American University of Nigera, Chidubem Arachie had spent a year as an exchange student at American University in Washington, D.C. Back in Nigeria, he graduated, taught high school math and computer science as a corpsman in the National Youth Services Corp in Lagos for a year and worked just shy of two years as a tax accountant at KPMG Nigeria.

Then he decided to take a serious look at Ph.D. programs. He said the EMBERS project at the Discovery Analytics Center is what drew him to Virginia Tech.

“I remember reading about EMBERS and thinking to myself how I would love to be involved in such a project, collaborating with researchers in various fields and schools,” said Arachie. “I was excited that Virginia Tech had such a center for interdisciplinary research and that my potential advisor, Bert Huang, was a DAC faculty.”

In 2017, he moved to Blacksburg to begin his Ph.D. focused on machine learning. Currently, he is working on a new algorithm — Adversarial Label Learning — that uses weak supervision to train a model robust to dependent/independent errors that can make accurate predictions without labeled data.

“The application of this line of research is limitless since, in the real world, labeled data is a limiting factor,” said Arachie. “Having access to a model utilizing only domain knowledge from experts is a useful tool for solving most problems.”

Arachie credits Huang with being a tremendous help in narrowing his area of research from the broad field of machine learning.

“Prior to starting the Ph.D. program I was interested in developing new algorithms to solve interesting real world challenges, but I was not sure how to go about it,” said Arachie. “After a lot of conversations with Dr. Huang, and through his relentless effort and guidance, I was able to focus on this area of machine learning research that combines theory and application.”

Arachie said the best thing about being a DAC student is having the opportunity to learn not only from his advisor but from others and being exposed to interesting work in various research areas.

“DAC creates an atmosphere that fosters interdisciplinary research and attending poster sessions gives me ideas about how I can apply my research to other fields and possibly collaborate with other labs within DAC,” he said.

His projected graduation date is Spring 2022, after which he would like to work in an industry research lab applying machine learning to solve real world problems.

“At some point I would also love to return to academia, maybe as an adjunct professor,” Arachie said.


DAC Student Spotlight: Yaser Keneshloo

Yaser Keneshloo, DAC Ph.D. student in computer science

A collaborative project with the Washington Post to predict the popularity of news articles kept Yaser Keneshloo busy after joining the Discovery Analytics Center in the spring semester of 2014.

“The Washington Post now uses this research as an internal tool for predicting the click-rate of a news article within 24 hours of publication,” said Keneshloo, who worked on this project with his advisor, Naren Ramakrishnan. He has also presented this work at the 2016 SIAM International Conference in a publication co-authored with his Washington Post collaborators.

Currently, Keneshloo spends some of his time working on a harder problem — automatic document summarization and machine translation — which requires knowledge in deep learning and natural language processing. The main objective, he said, is to build a model that generates automatic two to three sentence summaries from the content of a news article.”

One of the best things about being a DAC student, Keneshloo said, is being able to work toward solutions to a number of problems. “You are always involved with interesting projects from different government agencies and private companies,” he said. “And DAC tries to keep the projects related to your research to make the greatest impact.

Keneshloo graduated in 2012 from Iran University of Science and Technology with a master’s degree in software engineering with a specialization in artificial intelligence.

“Our world is now moving towards using artificial intelligence in almost every aspect of our daily life, from calling/texting your friends to making a restaurant reservation just by talking to your phone to making robots that could comprehend the surrounding area and react according to it,” Keneshloo said.

“Deep learning models are the building blocks for most of these ‘smart’ applications. Thus, working in this area allows me not only to understand how these real-world problems are being solved, but gives me a chance to propose new solutions for tasks that are yet to be solved by machines,” he said.

Keneshloo’s projected graduation date is Summer 2019. When he looks to the future, he sees himself working on other aspects of deep learning problems.

“So far, I have explored text summarization and machine translation problems, but there are many other problems that use deep models, such as speech synthesis, automatic cars, robotics, and recommender systems. Each of these problems has its own set of challenges and I am hoping that by combining knowledge from solving each specific task, one day we be able to offer a generalized model to do all these different tasks just as humans do,” he said.


Chandan Reddy receives 2018 Criteo Faculty Research Award

Chandan Reddy, associate professor of computer science and DAC faculty member

Chandan Reddy, an associate professor in computer science and a faculty member at the Discovery Analytics Center, has received a Criteo Faculty Research Award from the Criteo AI Lab.

This grant allows Reddy and his students to develop new computational techniques for some of the challenging problems that arise in the domain of computational advertising. Specifically, Reddy’s lab will be working on building deep learning based methods for the problem of identifying potential customers interested in a particular product based on the past activities in the entire customer pool. Deep learning is an important subfield of artificial intelligence.

The Criteo Faculty Research Award funds leading machine learning research at universities in order to improve collaboration between the Criteo AI Lab and academic faculty. The results of the funded research will be made available to the external machine learning community by publishing papers and/or open-sourcing any technology that is developed.

The award is provided as an unrestricted gift to the university. Reddy is one of eight awardees for 2018. All are full-time faculty members from universities that conduct research in machine learning related fields and award Ph.D. degrees to students working in that domain.


UrbComp student Davon Woodard spends summer in Data Science for the Public Good program, using data to improve communities

Davon Woodard, far left, and undergrad Cory Kim discuss their DSPG team findings with sponsor Wayne Strickland.

Davon Woodard has spent the past few months in the National Capital Region as a fellow for Data Science for the Public Good (DSPG). The program, launched and directed by the Social and Decision Analytics Laboratory (SDAL) at the Biocomplexity Institute of Virginia Tech, engages young scholars in conducting research at the intersection of statistics, computation, and the social sciences to determine how information generated within the community can be leveraged to improve quality of life.

Woodard, a Ph.D. student in the planning, governance, and globalization program in the School of Public and International Affairs and a graduate research assistant at the Global Forum of Urban and Regional Resilience, is also a research trainee in the National Science Foundation-sponsored Urban Computing (UrbComp) Certificate program administered through the Discovery Analytics Center. The UrbComp program trains students in the latest methods in analyzing massive datasets to study key issues concerning urban populations.

“I was attracted to the DSPG program by the challenge and opportunity of solving real-world issues,” said Woodard. “In many programs, students work on projects that will get put on a shelf and never seen again but DSPG moves beyond data science ‘practice.’ I knew that the projects that I had with DSPG were effecting front line service delivery and public policy.”

Woodard was one of six graduate fellows — and the only one from Virginia Tech — chosen through a competitive process. The summer program began in May and culminated on August 9 at a DSPG symposium held at the Virginia Tech Research Center — Arlington where students presented their research to their sponsors.

Each of the 15 DSPG project teams consisted of SDAL faculty, a graduate student, and undergraduate students from the Honors College at Virginia Tech.

“An additional advantage for our graduate fellows is that they gain leadership experience by managing and mentoring the undergraduate students on their project teams,” said Gizem Korkmaz, research assistant professor, and co-lead of the DSPG program at SDAL.

For their sponsor Wayne Strickland, director of the Roanoke Valley and Alleghany Region (RVAR) Commission, Woodard’s team identified factors that contribute to the attractiveness of the RVAR region with the goal to recruit and retain people to the workforce — both within and outside of the region. Within the region, the Commission is interested in identifying those who

are not currently in the labor force (i.e. early retirees, recent graduates) by improving job supply and demand match; providing transportation options; and improving housing quality. Outside of the region, they are interested in attracting individual and families to the region to build their workforce and foster economic development.

During the research process, the team accessed GIS shapefiles, geocoded locations of businesses and transportation routes, used multiple sources of federal statistics combined with local data, and identified issues through data analysis.

“Using American Community Survey data, we modeled synthetic populations on county and sub-county levels in the region for variables related to workforce development, economic development, and housing affordability,” said Woodard, “and while considering what attracts people to stay or come to an area, we further expanded our research to include health related issues like food insecurity, primary care providers, and obesity, as well as natural assets like air quality and greenways.”

Woodard helped create a workforce development composite index of “Regional Attractiveness” by neighborhoods to support local initiatives for both external and internal job force engagement. Early findings show that singles and families with college degrees live closer to the city of Roanoke and its surrounding areas, while singles and families with a high school education are more dispersed throughout the county. Transportation options are limited to vehicles for most residents in the region, and many face long commutes to their jobs .

Woodard’s second project was working with the Community Sponsor Network in Arlington County on the issue of equity, which identifies the disadvantaged by unmet needs for resources and services.

The DSPG team coupled data from the 2006-2016 American Community Service Data Census Bureau and data from the Bureau of Labor Statistics to research and analyze issues of equity in housing in Arlington County vis-a-vis middle-class and low-income worker earnings and local industry growth.

Arlington County is interested in formulating policies that keep people in Arlington. They want residents to earn a sufficient wage to assume middle class standing and be able to afford housing, Woodard said.

Based on the general recommendation to spend no more than 30 percent of gross monthly income (before taxes) on housing, the research team considered eviction rates and access to transportation and healthy food as well as statistics relating to jobs, salaries, and housing costs to determine an affordable price range for people renting or buying in the area.

“I am very happy to have had the opportunity to be part of the DSPG program,” said Woodard. “The UrbComp program’s curriculum, collaborations, and partnerships prepared me well to work with sponsors on a day-to-day basis and to use real-world data sets to help them find solutions to their community problems.”


Focus on Huijuan Shao…..a DAC alumnus interview

Huijuan Shao, DAC Ph.D. alumnus and research scientist at Hitachi America, Ltd.

Since graduating in 2016 with a Ph.D. in computer science, Huijuan Shao has transitioned from academia to industry. For nine months, she was a research associate at George Washington University where she developed regular expression models with Java to extract clinical variables from cancer pathology reports and tuned queries performance in PostgreSQL when searching from 8TB national electronic health records. In January 2018, her career took another path. She and her family moved west, to Santa Clara, California, where she joined Hitachi America, Ltd., as a research scientist, focusing on industrial AI.

Was moving from a university to a corporation a big change for you? 

It was actually more like going back to the familiar. After I earned my master’s degree from the University of Chinese Academy of Sciences in Beijing, I worked for six and a half years as an associate senior researcher in Hitachi’s research and development department in Beijing so I was not new to the business world.

What attracted you to Virginia Tech and DAC?

Data mining led me to Virginia Tech and DAC. My research interests are machine learning in time series, natural language processing and deep learning and its applications in the domain of sustainability and healthcare. Within those interests is a strong focus on supervised and unsupervised learning algorithms related to times series in urban computing.

How did you wind up in the Washington, D.C., area?

I began my Ph.D. program in Blacksburg in January 2011 but moved to McLean, Virginia, in 2014 when my advisor, DAC Director Naren Ramakrishnan, moved to the center’s Arlington location

What was the most exciting research you engaged in while at DAC?

My most exciting work while a Ph.D. student was to implement temporal mining algorithms to help save energy for sustainability, and discover social network sensor groups to predict the spread of epidemics in cities.

How are you using now what you learned at DAC?

Predictive analysis in industrial AI – which is what I do in my current position — proposes new data mining algorithms and applies existing machine learning algorithms to industrial datasets. This is strongly related to what I learned while at DAC.

Reflecting on your own experience, what advice would you give to current Ph.D. students?

Work hard and closely with your advisor. In my case, Naren had the most impact on me while I was a DAC student because he is an expert in this research area. In addition to guiding my research, he encouraged me when I met difficulties. I learned that both research direction and spiritual encouragement are very important.

I understand that you were also raising children while earning your Ph.D. That couldn’t have been easy. 

My three children were born while I was a student at DAC. Elaine is seven now and the twins, Franklin and George, are around two. I am very grateful for the continuous support from my parents and my parents-in-law.

Any other advice for current DAC students?

Industry internships can be very helpful if that is where you are headed. When I joined Hitachi, I found that several colleagues were recruited very quickly because they had previously interned here.

With a full-time job and three young children to care for, you probably don’t have much spare time.  But what do you like to do for fun?

Of course I am busy. Usually I get up very early in the morning, then read some books, or run or go hiking with friends. Every Sunday morning I hike with other VT alumni here and we talk about work, career, health, family, kids, and so on. I really enjoy these two to three hours of precious time.

 


DAC and UrbComp actively participating at KDD 2018 with conference organization and research presentations

KDD Logo

The Discovery Analytics Center and the Urban Computing Certificate Program (funded through a National Science Foundation traineeship grant and administered through DAC) will be well represented at the 24th Annual  Association for Computing Machinery Special Interest Knowledge Discovery and Data Mining (KDD 2018) conference in London, August 19-23.

The overall theme of this year’s conference is data mining for social good.

Chandan Reddy, associate professor of computer science and DAC faculty, served as a poster co-chair for the KDD conference.

Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and DAC director, served on the senior program committee for the KDD research track.

Aditya Prakash, assistant professor of computer science and DAC faculty, served on the committee for Health Day at KDD, held in conjunction with the conference, and is one of four organizers for epiDAMIK: Epidemiology meets Data Mining and Knowledge discovery, a Health Day workshop.

This workshop serves as a forum to discuss new insights into how data mining can play a bigger role in epidemiology and public health research. While the integration of data science methods into epidemiology has significant potential, it remains understudied, Prakash said.

The goal of the workshop is to raise the profile of this emerging research area of data-driven and computational epidemiology and create a venue for presenting state-of-the-art and in-progress results — in particular, results that would otherwise be difficult to present at a major data mining conference, including lessons learned in the “trenches.”

The paper, “Forecasting the Flu: Designing Social Network Sensors for Epidemics,” (B. Aditya Prakash; Naren Ramakrishnan; Huijuan Shao, K.S.M. Tozammel Hossain and Hao Wu, all DAC Ph.D. alumni; Madhav Marathe, professor of computer science and director of the Network Dynamics and Simulation Science Lab (NDSSL) at Virginia Tech; Anil Vullikanti, associate professor of computer science at NDSSL and Maleq Khan, assistant professor at Texas A&M University) will be presented at the epiDAMIK workshop by Prakash and Vullikanti.

An Urban Computing workshop is also scheduled in conjunction with KDD2018. The objective of this workshop is to provide professionals, researchers, and technologists with a single forum where they can discuss and share the state-of-the-art of the development and applications related to urban computing, present their ideas and contributions, and set future directions in innovative research for urban computing. It is particularly targeted to people who are interested in sensing/mining/understanding urban data so as to tackle challenges in cities and help better formulate the future of cities.

The following posters from DAC have been accepted for presentation at the workshop:

Additionally, a DAC alumnus, Prithwish Chakraborty, is running a third workshop taking place during the conference, Machine Learning for Medicine and Healthcare (MLMH).


Focus on Andrew Hoegh…..a DAC alumnus interview

Andrew Hoegh, DAC alumni and assistant professor of statistics at Montana State University

After Andrew Hoegh graduated from Virginia Tech with a Ph.D. in statistics in 2016, he headed northwest to Bozeman, Montana, to join Montana State University as assistant professor of statistics. That same year, there was more good news for Hoegh. “Bayesian Model Fusion for Forecasting Civil Unrest,”  which he co-authored with his DAC advisor Scotland Leman; DAC Ph.D. student Parang Saraf; and DAC Director Naren Ramakrishnan, garnered the Jack Youden Prize for Best Expository Paper in the 2015 issues of Technometrics, a journal published by the American Statistical Society.

In a recent interview Hoegh talked about life in Montana and reflected back on his time as a DAC Ph.D. student and brought us up to date.

You earned your B.A. from Luther College in Iowa and then went on to get your M.S. from Colorado School of Mines. What attracted you to Virginia Tech?

My prospective visit sealed the deal. I enjoyed my interactions with the statistics faculty and Virginia Tech offered me the very appealing opportunity of working on the massive and challenging problem of civil unrest. Also, I fell in love with Blacksburg.

What had the most impact on you while you were working toward your Ph.D. at DAC?

For me, the “what” is a “who.” Without a doubt, my advisor Scotland Leman had the largest impact on my professional career trajectory. His guidance offered the perfect balance of structure and freedom that allowed me to flourish as a researcher.

How are you using now what you gained from your DAC experience?

The ability to think through big, challenging problems and, through research, identify the best solutions has been invaluable as my interest in exploring spatial and spatiotemperal Bayesian statistical modeling continues. 

As you transitioned from Ph.D. student to professional academic, were there any real surprises?

While I certainly enjoy teaching, I did not realize that preparing lectures and grading student’s work are such solitary activities. At times, I miss the collaborative chaos of working as a graduate research assistant.

You mentioned a budding computer scientist in the family?

Yes, my six-year old daughter Eleanor loves to write code!  Both Eleanor and two-year-old Georgiana were born while I was getting my Ph.D. at DAC.

Any practical advice you can offer current Ph.D. students?

It might seem like you are already too busy, but take advantage of everything graduate school has to offer, both professionally and personally. I also recommend looking for prospective jobs as soon as you start school. This will enable you to identify your “dream jobs” and you can then build the necessary skills to be qualified for those positions upon graduation.

How do you like to spend your leisure time?

With my wife, Emma, and daughters, enjoying the great outdoors of Montana. In addition to her already mentioned interest in coding, Eleanor is quite an expert skier.

 

 

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UrbComp Ph.D. student Stacey Clifton credits conference with informing her dissertation research interests in intelligence-led policing

Stacey Clifton, UrbComp Ph.D. Trainee in Sociology

As a National Science Foundation trainee in the Urban Computing certificate program, Stacey Clifton, a Ph.D. student and sociology major, had the opportunity to attend the American Society of Evidence-Based Policing Conference last month.

The conference, held in Philadelphia, Pennsylvania, provided valuable information and insights related to her research on police socialization and subculture, and community, evidence-based, and predictive policing. Clifton said that what she learned enabled her to further pinpoint her dissertation research interests in intelligence-led policing.

“This conference was beyond beneficial for my studies, specifically due to the narrowed focus of sessions surrounding evidence-based policing,” she said. “The sessions were composed of academics and practitioners in the field covering topics from new evidence-based policing strategies to ethics surrounding these endeavors.”

Clifton said that the conference also provided the opportunity to network with many prominent individuals.

“Although this was the first time I’ve attended this conference, I do hope to continue my attendance in future years to stay abreast of new topics within this realm,” she said.

The Urban Computing certificate program is funded by a five-year grant from the National Science Foundation’s Research Traineeship Program, which encourages bold, new, potentially transformative, and scalable models for STEM graduate education training.

 

 


Jia-Bin Huang awarded NSF grant to advance representation learning and adaptation with free unlabeled images and videos

Jia-Bin Huang, assistant professor of electrical and computer engineering and a DAC faculty member

Jia-Bin Huang, an assistant professor of electrical and computer engineering and a DAC faculty member, has received a grant from the National Science Foundation’s Division of Information and Intelligent Systems to develop algorithms to capitalize on the massive amount of free unlabeled images and videos readily available on the internet for representation learning and adaptation.

This approach is in contrast to recent success in visual recognition which relies on training deep neural networks (DNNs) on a large-scale annotated image classification dataset in a fully supervised fashion.

“While the learned representation encoded in the parameters of DNNs have shown remarkable transferability to a wide range of tasks, depending on supervised learning substantially limits the scalability to new problem domains because manual labeling is often expensive and can sometimes require a specific expertise,” Huang said.

“Our aim in developing new methods is to significantly alleviate the high cost and scarcity of manual annotations for constructing large-scale datasets,” said Huang.

The study, entitled “Representation Learning and Adaptation using Unlabeled Videos,” commences this month and is estimated to extend through May 31, 2020.

The research team led by Huang will simultaneously leverage spatial and temporal contexts in videos taking advantages of rich supervisory signals for representation learning from their appearance variations and temporal coherence. Compared to the supervised counterpart (which requires millions of manually labeled images), learning from unlabeled videos is inexpensive and unlimited in scope.

The project also seeks to adapt the learned representation to handle appearance variations in new domains with minimal manual supervision. The effectiveness of representation adaptation is validated in the context of instance-level video object segmentation. Both graduate and undergraduate students will be involved in the project. Research materials will also be integrated into curriculum development in courses on deep learning for machine perception.

Results of the study will be disseminated through scientific publications, open-source software, and dataset releases. Huang joined Virginia Tech in 2016 as assistant professor of electrical and computer engineering. His research interests include computer vision; computer graphics; and machine learning with a focus on visual analysis and synthesis with physically grounded constraints.


Virginia Tech study identifies conspiracy cohorts on Reddit; suggests targeting ‘joiners’ for intervention

Tanushree Mitra, assistant professor of computer science and a faculty member at DAC

While online communities play a crucial role in spreading conspiracy theories after catastrophic events like mass shootings or a terrorist attack, not much is known about who participates in these event-specific conspiratorial discussions or how they evolve over time.

A new study by Tanushree Mitra, assistant professor of computer science and a faculty member at the Discovery Analytics Center, and Mattia Samory, a postdoc in the Department of Computer Science, identifies three conspiracy cohorts on the Reddit social news aggregation, web content rating, and discussion website and suggests that “joiners“ —  who join both Reddit and the conspiracy community only after an event has occurred — show the most extreme signs of distress at the time of an event and exhibit the most radical changes over time.

The other two user categories are “converts,” active Reddit users who join a conspiracy community after an event; and “veterans,” who are longstanding Reddit and conspiracy members.

“Since early press coverage typically lacks clear and definitive evidence, rumors and speculations surrounding an event increase as people attempt to rationalize the underlying complex phenomena and deal with a feeling of powerlessness,” Mitra said.

This is the first study to look at Reddit users/comments with respect to conspiracy.

“Our research found that joiners may be particularly predisposed to adopting conspiratorial attitudes. Organizations working towards dispelling conspiratorial beliefs would do best to focus their intervention efforts on joiners at the time of the event as that is when they show the most extreme signs of distress,” said Mitra.

For the study, Mitra and Samory focused on 10 years of more than six million comments in an active community of more than 200,000 users covering four tragic events — the Sandy Hook shooting, the Aurora theater shooting, the take down of Malaysia Airlines flight MH17, and the Boston Marathon bombing.

Following are other findings from their research:

  • Generally, discussions following a catastrophic event show signs of emotional shock, more complex language than usual, and simultaneous expressions of certainty and doubtfulness.
  • Joiners contribute the most verbose and least redundant comments, followed by veterans, who remain active for the longest time in Reddit and specifically on conspiracy.
  • Converts are least engaged in the conspiracy community.

Mitra and Samory will present their paper, “Conspiracies Online: User discussions in a Conspiracy Community Following Dramatic Events,” during the  12th International AAI Conference on Web and Social Media (ISWSM) in Stanford, California, June 25 to 28.

 


DAC students use summer months to broaden knowledge at tech-related jobs across the U.S.

Michelle Dowling, DAC Ph.D. student in computer science, teaching at her alma mater, Grand Valley State University.

Students at the Discovery Analytics Center have headed off to summer jobs and internships from coast to coast. Following is a good example of the kind of real world experience they are getting.

Payel Bandyopadhyay, a Ph.D. student in computer science, is working on data visualization at UPS Advanced Technology Group, Atlanta, Georgia, where she is helping redesign the UPS parcel tracker website. Her advisor is Chris North.

Jinwoo Choi, a Ph.D. student in electrical and computer engineering, is a computer vision researcher at NEC Labs America, Cupertino, California, working in the area of video understanding/action recognition. Choi’s advisor is Jia-Bin Huang.

Michelle Dowling, a Ph.D. student in computer science, is an instructor at her alma mater, Grand Valley State University in Allendale, Michigan.  She is co-teaching an introductory computer science course with Professor Roger Ferguson. Dowling’s advisor is Chris North.

Shuangfei Fan, a Ph.D. student in computer science, is a software engineer at Instagram in New York City. Her advisor is Bert Huang.

Abhinav Kumar, a master’s student in computer science, is an intern at PayPal in San Jose, California, where he is working on credit risk centric problems. His advisor is Edward Fox.

Tianyi Li, a Ph.D. student in computer science, is a software engineer at Cloudera, in Palo Alto, California, working on visual analytics for interpreting and better training machine learning models. Her advisor is Chris North.

Yufeng Ma, a Ph.D. student in computer science, is a research scientist at Yahoo! Research, in Sunnyvale, California, where he will apply deep learning techniques to data with both images and text. Ma’s advisor is Weiguo (Patrick) Fan and his co-advisor is Edward Fox.

Elaheh Raisi, a Ph.D. student in computer science is a data scientist on the Global Risk and Data Sciences team at PayPal in San Jose, California. This team is responsible for developing and enhancing machine learning and data mining capabilities, which are key to PayPal’s top-of-the-line data-driven decisions. Raisi’s advisor is Bert Huang.

John Wenskovitch, a Ph.D. student in computer science, is visualizing sequential content in multimodal documents/reports in team collaboration settings at FXPAL in Palo Alto, California. His advisor is Chris North.

Sirui Yao, a Ph.D. student in computer science, is a research scientist at Walmart in Bentonville, Arkansas.  She is working on a project that uses machine learning to build a hiring tool, an intelligent system that assists Human Resources in selecting candidate resumes. She will also study related issues such as fairness and security. Yao’s advisor is Bert Huang.

Xuchao Zhang, a Ph.D. student in computer science, will be researching argumentative zoning and note-taking behavior during document authoring at Microsoft Research AI, in Redmond, Washington.  Zhang’s advisor is Chang-Tien Lu.

Yuliang Zou, a Ph.D. student in electrical and computer engineering, will be a researcher at Adobe Research, San Jose, California. His advisor is Jia-Bin Huang.

Sneha Mehta, a Ph.D. student in computer science, is at the Netflix headquarters in Los Gatos, California, working on the open-ended problem of using Natural Language Processing (NLP) techniques to tangibly improve the quality of machine translated subtitles. Her advisor is Naren Ramakrishnan.

“Our DAC students greatly benefit from being out in the workforce during the summer months,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science and director of the Discovery Analytics Center. “In addition to contributing their skills to problems faced by companies, what they learn from these opportunities is invaluable and an important part of their graduate education.”


B. Aditya Prakash on IEEE magazine’s list of 10 young stars to watch in artificial intelligence

B. Aditya Prakash, DAC faculty member and assistant professor of computer science.

B. Aditya Prakash, an assistant professor of computer science in the College of Engineering, is being celebrated as one of 10 young stars in the field of artificial intelligence by IEEE Intelligent Systems.

The technical magazine named Prakash, who is also a faculty member at the Discovery Analytics Center, to the prestigious AI’s 10 to Watch list for his contributions to understanding, reasoning, and mining the phenomenon of propagation over networks in diverse real-world systems.  Click here to read more about the AI’s 10 to Watch list.


Congratulations to DAC spring and summer graduates!

DAC Ph.D. graduate Parang Saraf and his daughter Diya Saraf

Virginia Tech graduates celebrating their achievements this spring include two    Ph.D. students and three master’s students at the Discovery Analytics Center.

Two Ph.D. students and one master’s student are planning to celebrate the completion of their degrees during the summer.

Ph.D. May graduates

Liangzhe Chen, advised by Aditya Prakash, received a Ph.D. in computer science. His research interests are data mining, machine learning, sequence analysis, social media analysis and critical infrastructure systems. His dissertation is on “Segmenting, Predicting and Summarizing Data Sequences.” He is joining Pinterest in San Francisco as a machine learning engineer.

Parang Saraf, advised by Naren Ramakrishnan, received a Ph.D. in computer science. Saraf’s areas of research are text mining and information extraction. His dissertation is on “A Cost-Effective Semi-Automated Approach for Comprehensive Event Extraction.”

Master’s May graduates

Reid Bixler, advised by Bert Huang, received a master’s degree in computer science and applications. Probabilistic models is his main area of research and his thesis is on Sparse Matrix Belief Propagation.” In July, Bixler will join Amazon in Seattle, Washington, as a software engineer.

Sidney Holman, advised by Chris North, received a master’s in computer science. His thesis, “Entropy and Insight: Exploring how information theory can be used to quantify sensemaking in visual analytics,” is based on his work in the Information Visualization InfoVis lab. Holman has joined Sandia National Laboratories in Albuquerque, New Mexico.

Sanket Lokegaonkar, advised by Jia-Bin Huang, received a master’s degree in computer science. His areas of research are computer vision, continual learning, and machine learning and his thesis is on “Continual learning for Deep Dense Prediction.” Lokegaonkar worked on predicting driver state with dashboard cam and sensors with DAC and the Virginia Tech Transportation Institute.

Ph.D. Summer graduates 

Rupinder Paul Khandpur, coadvised by Naren Ramakrishnan and Chang-Tien Lu, is planning to graduate with a Ph.D. in computer science. His area of research is applied data sciences with an emphasis on query expansion, knowledge summarization and narrative generation from structured (newspapers) and unstructured (Twitter) texts. His dissertation is on “Augmenting Dynamic Query Expansion in Microblog Texts.” After graduation, he will join Moody’s Analytics as director of artificial intelligence/machine learning.

Yue Ning, advised by Naren Ramakrishnan, is planning to complete her Ph.D. in computer science this summer. Her dissertation is on “Capturing Precursors: Information Reciprocity, Event Modeling and Forecasting.” She will be joining the Department of Computer Science at Stevens Institute of Technology as a tenure-track assistant professor in the fall.

Master’s Summer graduate

Jeff Robertson, advised by Lenwood Heath, will receive a master’s degree in computer science at the end of the first summer session and will join Bloomberg in New York City as a software engineer. Robertson’s thesis is on “Entropy Measurements and Ball Cover Construction for Biological Sequences.”

 

 

 

 


DAC Student Spotlight: Lata Kodali

Lata Kodali, DAC Ph.D. student in statistics

Lata Kodali looks at statistics as a great bridge between theory and application.

“It is  also a field that is applicable in a broad spectrum,” she said,  “and right now I see myself working in an industry position with a focus on research and design that also encourages creativity.”

Kodali has a bachelor’s degree from Carson-Newman University and a master’s degree from Wake Forest University, both in mathematics. Prior to her Ph.D. work, most of her experience was theoretical rather than applied.

On a recommendation by her undergraduate advisor, who was a Virginia Tech alum, Kodali applied to Virginia Tech’s Ph.D. program in statistics. She applied to a few other graduate schools as well but, she said, the department visit sealed the deal.

“Everyone was very friendly and encouraging, and there is a variety of research interests within the department,” she said. The atmosphere felt warm rather than competitive, and fellow students really are colleagues rather than competitors.”

Kodali is working in the Bayesian Visual Analytics (BaVA) research group with her advisor and DAC faculty Leanna House.

Her current research focuses on the uncertainty in interactive displays of data created from Weighted Multidimensional Scaling (WMDS). WMDS is a linear projection technique to display high-dimensional data into a two-dimensional projection.

“The problem with current displays is that there is no information included about how imperfect the two-dimensional projection is,” Kodali said. “My current project is using Bayesian modeling to find a way to quantify this information and display it within an interactive visualization to help guide analysts in their data explorations.“

Kodali’s interest in this area of research was peaked while assisting House with user studies in the introductory statistics course STAT 2004. The BaVA research group developed a program that incorporates interactivity of WMDS displays, essentially a non-traditional learning tool, to see what kind of inferences students could make about the data without using formal statistics.

“It was interesting to see how novice analysts handle such explorations when there are no numbers involved and they have complete freedom to look at whatever they would like,” she said.

Kodali’s other research interests include regression and ANOVA,  social science, economics, biology and environmental science.

She is on track to graduate in 2020.

 

 


Virginia Tech graduate students team up with D.C. transit to help enhance customer service

UrbComp students Bryse Flowers (left) and Farnaz Khaghani were on the student team working with WMATA. Behind them is Brian Mayer, project manager and research scientist at the Discovery Analytics Center, who oversaw the study.

Last fall, the Washington Metropolitan Area Transit Authority (WMATA) struck a partnership with Virginia Tech’s graduate program in urban computing for help in predicting its system’s on-time performance (OTP).

The resulting study, by a team of students enrolled in Introduction to Urban Computing, a computer science course in the UrbComp certificate program administered by the Discovery Analytics Center, is one of the first steps in connecting WMATA’s Rush Hour Promise — initiated in January 2018 to provide a refund to any customer delayed by 15 minutes or more during rush hour — to underlying service disruptions, according to Jordan Holt, senior performance analyst at WMATA.  Click here to read more about the collaboration.


DAC Student Spotlight: Michelle Dowling

Michelle Dowling, DAC Ph.D. student in computer science

The desire to combine psychology with her knowledge and expertise in computer science in an interesting and challenging way drew Michelle Dowling toward her current research in human-computer interaction (HCI). This area of study allows her to focus on the cognitive (human) side of research rather than just on programming and computer science.

While exploring graduate program opportunities at Virginia Tech, Dowling, who earned a bachelor’s degree in computer science from Grand Valley State University, met DAC Associate Director Chris North. North introduced her to his research in information visualization and interactive data analytics tools. “I felt it was a perfect fit and decided to join Dr. North in his InfoVis Lab,” Dowling said.

Her research is focused on how to visualize and interact with high-dimensional data — more than three attributes/dimensions/properties of the individual data items, for example — contained in text-based documents, images, spreadsheets, or other various data sources. The sources are plotted onto a map using multi-dimensional scaling (MDS) algorithms. The parameters can then be upweighted or down weighted by the user to produce a different visualization.

“By its very nature, this research is extremely interdisciplinary, pulling from the psychology background in HCI; current research from collaborators in the Statistics department; and domain experts or end users who will use the data analytics tools we create,” Dowling said.

She is also a National Science Foundation research trainee in the UrbComp program administered through DAC.

Dowling will receive an M.S. in computer science in May. Her Ph.D. is on target for spring 2020. After graduation, she is looking toward an academic career. This summer, Dowling is co-teaching an introductory course to computer science at her alma mater in Allendale, Michigan.


DAC Student Spotlight: Tian Shi

Tian Shi, DAC Ph.D. student in computer science

When Chandan Reddy, associate professor in computer science, joined the DAC faculty in the National Capital Region in August 2016, one of his Ph.D. students, Tian Shi, moved right along with him.

“I feel very lucky to be Dr. Reddy’s student. He has helped me very much in both my research and life,” said Shi.

A Ph.D. in computer science will be the second Ph.D. for Shi.  His first, from Wayne State, is in physical chemistry.

Shi’s research was in theoretical and computational chemistry built upon quantum mechanics, statistical physics, and ab initio calculations. Various projects led him to computer science, where he found an interest in data mining, machine learning, and data visualization.

“There are many opportunities in this interdisciplinary area, such as applying machine learning to traditional computational chemistry,” said Shi. “During my Ph.D. studies in computer science I will focus on my research projects in text mining and will be trying to apply what I have learned in physical chemistry to data mining.”

Shi is interested in developing new algorithms to discover knowledge from text data. One of his current research projects involves topic modeling, a powerful tool in discovering hidden semantic structures from a collection of text documents.

“Every day, large numbers of short texts are generated, such as tweets, search queries, questions, image tags, and ad keywords and they play an important role in our daily lives,” said Shi. Discovering knowledge from them is an interesting and challenging research focus because short texts consist of only a few words and they are arbitrary, noisy, and ambiguous.”

More conventional methods are designed to discover topics from long documents but have some difficulty in capturing semantics for short text due to the lack of abundant word correlations, Shi said.

The non-negative matrix factorization based algorithm he proposes in his research tries to tackle this problem by leveraging a recently advanced word embedding technique. The proposed models have achieved significant improvement in quality over conventional methods in terms of word coherence and document representation. A paper he collaborated on about this research has been accepted by WWW 2018 conference in Lyon, France, next week.

“I have benefited greatly from Dr. Reddy, who guided me to this area of research and shared a lot of his knowledge with me,” said Shi. “I have also benefited from discussions with my colleagues, and from group meetings and seminars. All have helped me gain comprehensive knowledge and deeper understanding of the research areas I am interested in.”


Focus on Alex Endert…..a DAC alumnus interview

Alex Endert, DAC Ph.D. alumnus and an assistant professor in the School of Interactive Computing at Georgia Tech

While a student at DAC, Alex Endert (Ph.D. computer science 2012) worked with his advisor Chris North on a user interaction technique for visual analytics (semantic interaction) that helped adjust analytic models by computing on simple, well-understood interactions. For example, by highlighting a phrase of text or grouping a pile of documents adjusts underlying algorithms they can help people without data science training make sense of large amounts of text quickly. This line of research ultimately led to Endert’s dissertation, and grounds much of his research today.

Since 2014, Endert has served as assistant professor in the School of Interactive Computing at Georgia Tech. He is a recent recipient of two major awards, the prestigious National Science Foundation (NSF) CAREER award and a $2.7 million grant from the Defense Advanced Research Projects Agency (DARPA) Data-Driven Discovery of Models (D3M) program to develop new techniques to make machine learning in data science more accessible to non-data scientists.

In an interview, Endert shared some thoughts about his experiences at DAC, the best part of his job, and a few personal snippets. 

 

How did you wind up at DAC?  

Honestly, I was browsing the lab websites, saw Dr. Chris North’s site, and saw it had a massive, 50-monitor large display. I thought working on such technology would be awesome. Interestingly enough, my dissertation ended up having less to do with large displays, but I recall that being one of the reasons I was initially interested in Virginia Tech and DAC. So, I went up to Blacksburg and chatted with Chris.

So your advisor had a lot to do with your decision?

Yes, meeting Chris ultimately led to my decision. The advice I got from many colleagues and current students is that having a similar style of research as your advisor is important, and in the short time meeting Chris, I got that sense.

You worked at Pacific Northwest National Laboratories for two years before joining Georgia Tech.  What brought you back to academia?

It was a wonderful experience. I was able to perform applied research, work with really great people, and learn a lot from many of them. But I missed working with students and that is what led me back to academia. Mentoring Ph.D. students, and helping them achieve their career goals is what I like best about my job. As a DAC student I learned many skills about how to be an effective advisor. Thanks Chris!

How else did your experience as a Ph.D. student influence you?

I often reflect on my time at Virginia Tech and DAC. Beyond the advising skills I already mentioned, research accomplishments, and graduating successfully, I recall many experiences that helped shape my research interests. For example, the multi-disciplinary nature of the Discovery Analytics Center connected me with colleagues outside of my immediate area of research and illuminate challenges at the intersection of HCI, visual analytics, and data science. Those challenges are becoming more important as our culture becomes more data-driven.

Any advice for current DAC students?

Take advantage of having students and professors nearby who are not directly in your area of research. Chat with them over coffee about your work, and listen to their feedback. When you graduate, it is likely that you will be communicating or selling your research to people who are in nearby — but not identical — fields.

What is the most important thing you learned at DAC?

While impactful research is challenging, it can also be fun!

Speaking of fun, any interests/hobbies?

I have grown to enjoy hobbies that get me away from technology, such as camping, fishing, golf, hiking, etc. My most recent experience was going ice fishing for the first time. That was great, but perhaps a little too cold for my liking.

What is the one thing you would like people to know about you?

I still pull for Virginia Tech football. Let’s Go, HOKIES!


DAC Student Spotlight: Yue Ning

Yue Ning, DAC Ph.D. student in computer science

“Working in data science and machine learning is exciting, but it is even more exciting when science helps us solve real-world challenges,” said Yue Ning, a Ph.D. student in the computer science department.

The opportunity to be involved in high impact research drew Ning to Virginia Tech and DAC. “I am fortunate and honored to be working with Dr. Naren Ramakrishnan, who is one of the leading researchers in data analytics and applied machine learning,” she said.

Ning’s interest in computer science evolved from her love of math and puzzles in elementary school.

“When I first discovered the computer, I was attracted to the beauty of its processing power and multiple fascinating functions. Without a doubt, I chose to study computer software when I enrolled in college,” Ning said. “And that is when social media really took off.”

Since then, she said, the world has become more and more connected, generating accessible data at massive scales. Data-driven models are motivated by, and have contributed to, many domains including social informatics, security, games and health.

“I believe in data and find myself especially interested in data-driven machine learning and AI applications. The area has provided tons of opportunities for computer scientists to explore with the help of innovative algorithms. I am always excited to learn cutting-edge theories, models, and applications in this big data era,” Ning said.

Her research focuses on applying machine learning algorithms to solve real world problems such as forecasting societal events as well as predicting users’ behaviors in online services. Ning’s thesis is about discovering precursors for the use in event modeling and forecasting. A key problem of interest to social scientists and policy makers is modeling and forecasting large-scale societal events such as civil unrest, disease outbreaks, and turmoil in economic markets. Forecasting algorithms are expected not only to make accurate predictions, but also to provide insights into causative attributes that influence an event’s evolution.

“With the machine learning paradigm known as multi-instance learning I have been studying and developing frameworks that discover event precursors,” said Ning. “Using large-scale distributed representations of news articles and multi-task learning, I can demonstrate how this framework can provide clues into the spatio-temporal progression of events.”

Ning, who received a master’s degree in computer science and applications from the Graduate University of Chinese Academy of Sciences is expecting to graduate in summer 2018 and join the Department of Computer Science at Stevens Institute of Technology as an assistant professor in the fall.

Among other accomplishments while a Ph.D. student, earlier this year, Ning received a Student Travel Award to attend the SIAM International Conference on Data Mining; was invited to serve on the program committee for the Advances in Social Networks Analysis and Mining (ASONAM) conference; and had a paper accepted by the ACM Transactions on Knowledge Discovery from Data (TKDD).


Tanu Mitra awarded NSF grant to study how people relate to online news

Tanushree Mitra, DAC faculty member and assistant professor of CS

Tanushree (Tanu) Mitra, an assistant professor of computer science and a DAC faculty member, has received a grant from the National Science Foundation supported by the Division of Information and Intelligent Systems to lead a study that will use social computing and human-centered approaches to better understand the relationship between people and technology in the context of online news.

“The aim is to provide new perspectives that address digital misinformation by focusing on how we can establish differences between mainstream sources and misleading sources of online news and how we can nudge people to be more careful and conscious consumers of online news,” said Mitra.

The study, entitled “Empirical and Design Investigations to Address Misleading Online News in Social Media,will be conducted along two symbiotic lines of inquiry.

Using data from a professionally curated list of online news sources, along with credibility labels from expert fact-checkers, and tweets sent out by these news sources over a period of at least a year, the researchers will empirically investigate misleading online news sources.

“We will look at how the topical and writing style of these misleading online sources differ from mainstream sources, how the user distinguishes between them and any corresponding time-related changes,” said Mitra.

The second thrust of the study will explore design interventions to increase people’s awareness while they read news on social media sites. Specifically, it will investigate two classes of design nudges on Twitter.

The first intervention, “emphasize,” will nudge users to reflect on the ambiguity and uncertainty present in certain news posts and will automatically detect whether a social media news post from a mainstream source has been questioned and highlight those questionable tweets for the news reader. For example, several users questioned a report from the Associated Press that United Emirates orchestrated the hacking of a Qatari government news site, asking how the AP knows this.

The second intervention, “de-emphasize,” will be triggered whenever news posts originate from misleading sources to make that post less visible in an attempt to minimize the user’s exposure to it.

“The human-centered evaluations accompanying these interventions will provide qualitative and quantitative evidence about user experiences, as well as measurements of their efficacy,” Mitra said.

Mitra joined Virginia Tech in 2017 after earning a Ph.D. in computer science from the Georgia Institute of Technology where the GVU Center named her a Foley Scholar, the highest award for student excellence in research contributions to computing.


Research teams led by junior faculty win seed funding for new projects

Tanushree Mitra, DAC faculty member and assistant professor of CS

Congratulations to Tanushree Mitra, a winner in the latest round of Junior Faculty Awards from the Institute for Critical Technology and Applied Science.

Mitra, a faculty member at the Discovery Analytics Center and assistant professor in the Virginia Tech – Department of Computer Science, will lead, with James Hawdon at the Virginia Tech College of Liberal Arts and Human Sciences, a study on the language of online extremism: Computational models for discovery and analysis of framing around extremists’ narratives. Click here to read more about Mitra’s award.


DAC Student Spotlight: Raja Phanindra Chava

“You have to work every day at being the best you can be. It is a project that is never-ending.”

These are Raja Phanindra Chava’s own words — and his inspiration —  as he pursues an M.S. in computer engineering.

“I believe that learning is a constant process throughout life to achieve excellence,” said Chava, “and it is my primary driving force.”

 

 

After graduating with a bachelor’s degree in electrical/electronic engineering from SASTRA University in India, Chava said he realized that undergraduate studies would not be enough for him.

“I wanted to do research where major innovations take place. Virginia Tech is one of the best graduate institutions for research in the field of deep learning and graphs and that is what brought me to the university and to DAC,”  he said.

Deep learning — now being used successfully in many technological areas — has always been Chava’s area of interest and integrating deep learning with network comparison using neural networks is where he finds the potential to be particularly innovative. He credits his advisor, Srijan Sengupta, with helping to guide him through the right application and approach to his research.

“When given two or more graphs/networks, I am trying to find out the degree of similarity between them,” Chava said. “Social networking has become a major force in the contemporary world and networking is all about connections. If you look at connections between people in social networks from a research perspective, they are nothing but graphs with people as nodes and connection between them as edge. It would be great if we could compare connections between people from various social media networks.”

Chava’s goal is to work for a Fortune 500 company in a position that aligns to his research interests.  His projected graduation is May 2019.


DAC Student Spotlight: Yuliang Zou

DAC Ph.D. student, Yuliang Zou

Do you think working with image and video would make an interesting career?

Yuliang Zou definitely does. The first-year Ph.D. student — who would like to join the research arm of a major company one day — is researching computer vision, trying to teach computers to analyze and think like a human when they are given visual data like still images, RGB-D data, or video sequences.

“The computer can recognize objects in the image,” said Zou, who is majoring in computer engineering. “Recent years have witnessed significant progress in this domain as mainstream methodology changes from traditional hand-crafted features to data-driven methods, often referred to as deep learning.

“The main drawback is that we require a lot of annotated data to train the models to perform specific tasks like image classification, object detection, etc. So we are interested in finding an alternative approach to training such models, which can alleviate the requirement of annotations while achieving performance comparable to those models trained with full annotations,” he said.

Last fall, Zou presented “Label-Efficient Learning of Transferable Representations across Domains and Tasks” (collaborating with Stanford University and the University of California, Berkeley) at the 2017 Conference on Neural Information Processing Systems (NIPS) in Long Beach, California, and received a Travel Award from the organization.

Zou’s advisor is Jia-Bin Huang, who was significant in drawing him to Virginia Tech.

“When you are choosing a Ph.D. program, your advisor is the most important factor,” said Zou. “Professor Huang is a rising star in this research area and our research interests are aligned as well.”

This summer, he will intern at Adobe in San Jose, California. Zou anticipates graduating in 2022


UrbComp student team takes second place in Pamplin ethics competition

Students and judges at 2018 Data Ethics Case Competition are (front row) Rob Day, Techlab; Stacey Clifton; and Matt Slifko; (back row) Rich Wokutch, professor of management; Davon Woodard; and John Grant, Palantir.

Three Ph.D. students in the Urban Computing Certificate (UrbComp) program decided that the 2018 Data Ethics Case Competition would be a good way to apply what they have been learning in one of the program’s courses, GRAD 5134: Ethics and Professionalism in Data Science, this spring.

So they teamed up to enter the competition, sponsored by the Center for Business Intelligence & Analytics, which bridges classroom learning with a real-life situation and important questions for the future and encourages diverse trans-disciplinary teams.

The UrbComp team — Stacey Clifton, a sociology major; Matthew Slifko, a statistics major, and Davon Woodard, a student in the planning, governance, and globalization program in the School of Public and International Affairs and a graduate research assistant at the Global Forum of Urban and Regional Resilience – were awarded second place during the final round of the case competition. The award carries a $1,500 scholarship.

The competition began in February. The teams were given a case history that included two pending projects a company could choose from and asked to analyze the opportunities, ethics, and potential risks of the decisions; recommend how, or whether, to proceed with these projects; and carefully explain the reasons for the recommendations because they may be used to construct criteria for making project decisions in the future. Each team created a three-page executive summary and made a final presentation last Friday.

The Ethical Data Decisions in Practice competition was initiated this year. It was also sponsored in part by Palantir Technologies, the Pamplin Business Leadership Center, Cherry Bakaert, Partners in Financial Planning, and the Pamplin College of Business.


Urban computing program provides Ph.D. students with valuable skills to address problems faced by cities

UrbComp Ph.D. students, left to right top, Nikhil Muralidhar and Gloria Kang; bottom, Stacey Clifton and Davon Woodard

As increasing numbers of people move to cities and become more wired and networked, Ph.D. students across various academic disciplines at Virginia Tech are joining together to focus on how data science can help them find solutions to urban problems. Click here to learn more about these students and their research.

 

 


DAC Student Spotlight: Xuchao Zhang

DAC Ph.D. student, Xuchao Zhang

In the era of data explosion, noise and corruption in real-world data caused by accidental outliers, transmission loss, or even adversarial data attacks is inevitable and often results in incorrect data labeling. For example, a negative review in the Internet Movie Database (IMDb) could be mislabeled as positive or an image of a panda might be mislabeled as a gibbon.

Xuchao Zhang, a Ph.D. student in computer science, is focused on solving the problem of mislabeling.

“Using scalable robust model learning, we propose distributed and online robust algorithms to handle regression and classification problems in the presence of adversarial data corruption,” said Zhang, who is advised by Chang-Tien (C.T.) Lu in the National Capital Region.

Zang said his research can be broadly applied to noisy datasets in massive real-world applications.

Zhang, who earned a bachelor’s degree at Shanghai Jiao Tong University in China, begin his Ph.D. studies in 2009.

“I chose Virginia Tech’s engineering school for its abundance of advanced research resources and outstanding faculty in the field of data mining and machine learning,” Zhang said. “I am very fortunate to work with Dr. Lu as a DAC student.”

He collaborated with Lu and other researchers from Virginia Tech and George Mason University on the study, “Online and Distributed Robust Regressions under Adversarial Data Corruption,” which he presented at the 2017 IEEE International Conference on Data Mining (ICDM) in New Orleans, LA, in November.

His research has also been presented at other conferences, including the ACM International Conference on Information and Knowledge Management (CIKM); the IEEE International Conference on Big Data, and the International Joint Conference on Artificial Intelligence (IJCAI).

Zhang serves on the program committee (research track) for the Association of Computing Machinery’s Special Interest Group on Knowledge of Discovery and Data Mining (KDD) and will be attending the 2018 conference in London.

This summer, Zhang heads to Redmond, Washington, where he has an internship at Microsoft Research AI.


DAC Student Spotlight: Elaheh Raisi

DAC Ph.D. student, Elaheh Raisi

Elaheh Raisi’s enthusiasm for math dates back to high school. So it was not surprising when Raisi chose applied mathematics as her major at the Amirkabir University of Technology -Tehran Polytechnic.

“I realized early on that mathematics is essential for many practical sciences,” said Raisi. “My aim was to gain a strong knowledge of mathematics that I could use in problem solving.”

During her freshman year Raisi concentrated on mathematics and programming-related courses but after taking some computer science classes, she developed an interest in artificial intelligence. She earned a master’s degree in artificial intelligence at the Science and Research branch of the Islamic Azad University.

Raisi chose to pursue a Ph.D. in computer science at Virginia Tech because of “the abundance of advanced research resources and facilities in the field of data mining, a supporting environment at a prestigious university, and outstanding professors.” Now, a fifth-year Ph.D. candidate, Raisi is advised by Professor Bert Huang and works on cyberbullying detection on social media in his Machine Learning Laboratory.

To address the computational challenges associated with designing automated, data-driven machine learning approaches for harassment-based cyberbullying detection Raisi and Huang have developed a weakly supervised framework, which is specialized for cyberbullying detection,” she said.

The framework consists of two learning algorithms to improve predictive performance by taking into account not only language, but also social structures. One learner identifies bullying incidents by examining the language content in the message; another learner considers social structure to discover bullying. Intuitively, each learner is using different body of information. The learning algorithm tries to make them eventually agree whether social interactions are bullying.

“Our research is geared toward a very important topic in any online automated harassment detection: fairness against particular targeted groups including race, gender, religion, and sexual orientations,” said Raisi. “Our goal is to decrease the sensitivity of models to language describing particular social groups.”

For their research, Raisi and Huang won a best paper award at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 2017.

They also won a best paper award at the Learning with Limited Labeled data (LLD) workshop at NIPS, 2017 for including deep learning methodologies (word and node embedding) into their framework.


DAC Student Spotlight: Jonathan Baker

Jonathan Baker, DAC Ph.D. student

Jonathan Baker earned a master’s degree in computational and applied math at Rice University in Houston, Texas, in 2015.

When Mark Embree, one of his professors at Rice, returned to his alma mater in Blacksburg to lead the Computational Modeling and Data Analytics program in the College of Science Academy of Integrated Science, Baker did not hesitate to follow him.

“Once I decided that I wanted to pursue a Ph.D. in math,” he said. “I knew the only professor I wanted to continue down that path with was Mark Embree.”

So Baker applied to Virginia Tech as a Ph.D. student in the Department of Mathematics. Advised by Embree, a professor of mathematics and DAC faculty member, Baker is studying how best to track the changes in vibration patterns over time, an extension of his existing research on spectral theory in linear dynamics and control.

“Monitoring vibrations is important for detecting changes and damage in buildings, bridges, and other structures,” said Baker, who is also a National Science Foundation research trainee in the UrbComp program administered through DAC.

Baker’s research is taking place in the College of Engineering’s flagship Goodwin Hall. There, roughly 240 accelerometers attached to 136 sensor mounts throughout the building’s ceilings detect information on where people are within the structure, measure normal structural settling and wind loads, and track building movement resulting from earthquakes similar to the event that struck Virginia in 2011. A sensor array mounted outside the building measures external vibrations, such as wind, the bustle of traffic on nearby Prices Fork Road, the thunderous boom of tens of thousands of Hokie fans celebrating a touchdown at Lane Stadium, and possible seismic activity.

In February 2016, Baker authored Strong Convexity Does Not Imply Radial Unboundedness in The American Mathematical Monthly. He has also contributed to the American Math Society’s grad student blog.

Baker earned his undergraduate degree in math at Brigham Young University.


DAC Student Spotlight: Subhodip Biswas

 

Screenshot of LCPS map on the crowdsourcing website Biswas created.

Subhodip Biswas, DAC Ph.D. student

 

 

 

 

 

 

 

 

 

The omnipresent activity of school redistricting is driving Ph.D, student Subhodip Biswas’s research at the Discovery Analytics Center.

“Through blogs and news articles, I became aware that school redistricting happens in some US public school systems almost every year,” said Biswas, who earned a bachelor ‘s degree in electronics and telecommunication engineering from Jadavpur University in India in 2014. “It was fascinating to learn how numerous considerations go into designing new school zones.”

“I took a deep dive into this area, learning more and more about the process,” he said. “My interest cemented even further when I attended the Loudoun County Public School’s rezoning meetings last fall.”

Biswas, advised by Naren Ramakrishnan, uses data-driven methodologies to better understand the process and thinks about helping citizens come up with alternative redistricting plans that meet their needs.

For Loudoun County, Biswas designed a crowdsourcing platform for parents whose children would be affected by the redistrict. Using this website, parents could visualize school zone maps and proposed changes; understand how the changes would affect people in each neighborhood; see the most popular plans; share their own opinions; learn what others think; and even submit their own plans.

DAC and Biswas are exploring opportunities to use a similar crowdsourcing platform with other area school systems who are undergoing redistricting.

“Through my research I aim to bring computational support and transparency to the process of school rezoning by showing parents the considerations that go into making these plans,” said Biswas, who is projected to receive his Ph.D. in computer science in 2019.

Biswas said that at DAC he has been able to assimilate knowledge from various areas like political science, geographical information systems, spatial data mining, education, and crowdsourcing.

“Using this unique set of knowledge, I want to go into academia and make a difference,” Biswas said.  “I feel that data science has a lot of areas yet to be explored and I would like to devote my professional career to doing that.”


B. Aditya Prakash receives prestigious NSF CAREER Award

B. Aditya Prakash, assistant professor in The Department of Computer Science has received the prestigious Faculty Early Career Development (CAREER) Award from the National Science Foundation to find data-driven network strategies to enhance national security and public health. Click here to read ore about Aditya’s award.


DAC Student Spotlight: Jeff Robertson

DAC M.S. student Jeff Roberston

Jeff Robertson grew up in Blacksburg and is the fifth Hokie in his family. “So, it was not difficult for me to choose Virginia Tech,” he said.

Working towards a master’s degree in computer science applications, Robertson’s current research is part of the Fun GCAT project in collaboration with the Biocomplexity Institute of Virginia Tech.

Within that larger program, his focus is on developing a new tool that can efficiently index and search massive biological data sets.

“The idea I’m investigating is based on the fact that these databases of DNA and protein sequences are relatively low entropy for their size, meaning that they have some inherent redundancy due to their biological nature,” he said. “I am researching what techniques can be incorporated into a tool so that the query time and index size are proportional to the entropy of the data set instead of its size.”

Robertson — who earned a bachelor’s degree in computer science at Virginia Tech — was introduced to this type of work in an undergraduate course, Intro to Computational Biology and Bioinformatics. That led to a senior capstone project in the same study area and his interest has only grown from there, he said.

Lenwood Heath, a professor in the Department of Computer Science and DAC faculty member, taught the undergrad course that influenced Robertson’s academic path and is now his advisor.


Edward Fox receives Albert Nelson Marquis Lifetime Achievement Award

Edward Fox, Professor of Computer Science

Marquis Who’s Who, the world’s premier publisher of biographical profiles, has presented Edward Fox with the Albert Nelson Marquis Lifetime Achievement Award, in recognition of outstanding contributions to his profession and the Marquis Who’s Who community.

In an announcement, the organization said, “An accomplished listee, Dr. Fox celebrates many years’ experience in his professional network, and has been noted for achievements, leadership qualities, and the credentials and successes he has accrued in his field. As in all Marquis Who’s Who biographical volumes, individuals profiled are selected on the basis of current reference value. Factors such as position, noteworthy accomplishments, visibility, and prominence in a field are all taken into account during the selection process.”

Fox has been a professor in the Department of Computer Science since 1983 and is also a Discovery Analytics Center faculty member.

Since 1899, when A. N. Marquis printed the First Edition of Who’s Who in America, Marquis Who’s Who has chronicled the lives of the most accomplished individuals and innovators from every significant field of endeavor, including politics, business, medicine, law, education, art, religion and entertainment.


Congratulations to DAC graduates!

Ed Fox (left) with Yinlin Chen (right).

Ian Crandell (left) with Scotland Leman (right).

Virginia Tech graduates celebrating their achievements this fall included seven Ph.D. students at the Discovery Analytics Center.

Yinlin Chen received a Ph.D. in computer science and applications and was hooded by his advisor, Edward Fox. Chen’s dissertation, “A High-quality Digital Library Supporting Computing Education: The Ensemble Approach,” was on developing an application pipeline to acquire user-generated computing-related educational resources from YouTube and SlideShare for an educational Digital Library combining transfer learning and crowdsourcing (Amazon Mechanical Turk). He proposed cloud-based designs and applications to ensure and improve these qualities in DL services using cloud computing. Chen works at the Virginia Tech University Libraries, where he has been employed as a software engineer while earning his Ph.D.

Ian Crandell received a Ph.D. in statistics and was hooded by his advisor, Scotland Leman. His dissertation, “Semi-Supervised Anomaly Detection and Heterogeneous Covariance Estimation for Gaussian Processes,” applied methods from spatial statistics to detect anomalous readings in networks of correlated sensor systems. By using a novel correlation based distance metric, he was able to automatically identify anomalous readings based on the past readings of a sensor as well as other sensors in the network. His method also allows for the incorporation of expert knowledge using manual flagging of a small subset of anomalous points. Crandell has joined the Social and Decision Analytics Laboratory in the Biocomplexity Institute of Virginia Tech as a postdoctoral associate and is located in the National Capital Region.

Sherin Ghannam received a Ph.D. in computer engineering and was hooded by her advisor Lynn Abbott. In her dissertation, “Multisensor Multitemporal Fusion for Remote Sensing using Landsat and MODIS Data,” Ghannam cites that the growing Landsat data archive represents more than four decades of continuous Earth observation. Landsat’s role in scientific analysis has increased dramatically in recent years as a result of the open-access policy of the U.S. Geological Survey. However, this rich data record suffers from relatively low temporal resolution due to the 16-day revisit period of each Landsat satellite. She proposes that data-fusion approaches to estimate Landsat images at other points in time combine existing Landsat data with images from other sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS) from the Terra and Aqua satellites. MODIS provides daily revisits, however, with a spatial resolution that is significantly lower than that of Landsat.  Ghannam has relocated to Egypt since graduating in December.

Abhijit Sarkar received a Ph.D. in electrical engineering and was hooded by his advisor, Lynn Abbott. His dissertation, “Cardiac Signals: Remote Measurement and Applications,” investigates the promises and challenges for application of cardiac signals in biometrics and affective computing, and noninvasive measurement of cardiac signals and mainly discusses two major cardiac signals: electrocardiogram and photoplethysmogram. Sarkar’s major research interests include cardiac biometrics, remote plethysmography, computer vision, machine learning, affective computing, driver monitoring and face biometric anti-spoofing.  Currently, he is working at the Virginia Tech Transportation Institute, where he was a research assistant while earning his degree.

Saurav Ghosh received a Ph.D. in computer science and applications. His dissertation, “News Analytics for Global Infectious Disease Surveillance,” focuses on developing digital surveillance tools that can perform automated (near) real-time mining of online news reports (unstructured or semi-structured) for monitoring and forecasting infectious disease dynamics in populations at diverse geographical regions of the world. His advisor was Naren Ramakrishnan. Ghosh is currently a Natural Language Processing (NLP) data scientist at Exovera, a subsidiary at SOS International LLC, in Reston, Virginia.

Andrew McCaleb “Caleb” Reach received a Ph.D. in computer science and applications.  In his dissertation, entitled “Smooth Interactive Visualization,”  Reach developed a formal methodology for smoothness in interactive visualization based on signal processing theory. While a graduate student, he worked at the InfoVis Lab; his advisor was Chris North. He is now working at Google in New York City.

Hao Wu received a Ph.D. in electrical and computer engineering. His dissertation, “Probabilistic Modeling of Multi-relational and Multivariate Discrete Data,” studied and addressed three problems involving the modeling of multi-relational discrete data and multivariate multi-response count data, namely, discovering surprising patterns from multi-relational data; constructing a generative model for multivariate categorical data; and simultaneously modeling multivariate multi-response count data and estimating covariance structures between multiple responses. Wu’s co-advisors were Naren Ramakrishnan and Lynn Abbott. Wu is a software engineer at Google in San Francisco.


Edward Fox named 2017 ACM Fellow

Edward Fox, Professor in the Department of Computer Science and DAC faculty member, has been named a 2017 Association for Computing Machinery Fellow for his contributions to information retrieval and digital libraries.  Click here to learn more about about his history of service at ACM.


NIPS Conference 2017 showcases work from DAC Ph.D. students

 

 

 

 

 

 

 

 

 

 

 

 

A group of Ph.D. students from the Discovery Analytics Center headed with their faculty advisors to Long Beach, California, last week to present papers and posters at the 2017 Conference on Neural Information Processing Systems (NIPS). One of the workshop papers was distinguished with a Best Paper Award and two of the students received NIPS Travel Awards.

2017 marks the 31st year for the international multi-track machine learning and computational neuroscience conference includes invited talks, demonstrations, symposia, and oral and poster presentations of refereed papers, and workshops.

The Women in Machine Learning Workshop was held in conjunction with this year’s NIPS conference and DAC students presented during that event as well.

At the main conference, Sirui Yao presented “Beyond Parity: Fairness Objectives for Collaborative Filtering” (Yao and Bert Huang, assistant professor of computer science). An overview video for the paper can be viewed on YouTube.

DAC faculty Jia-Bin Huang, assistant professor of electrical and computer engineering collaborated on two papers which were also presented at the main NIPS conference: “Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks” (with the University of California MERCED); and “MaskRNN: Instance Level Video Object Segmentation” (with the University of Illinois in Urbana-Champaign).

Yuliang Zou presented “Label-Efficient Learning of Transferable Representations across Domains and Tasks” (Zou collaborating with Stanford University and the University of California, Berkeley).

Both Yao and Zou received NIPS Travel Awards.

A Best Paper award went to “Co-trained Ensemble Models for Weakly Supervised Cyberbullying Detection” (Elaheh Raisi and Bert Huang), presented by Raisi during the conference workshop on Learning with Limited Labeled Data: Weak Supervision and Beyond.

“Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight” (Jia-Bin Huang and Yen-Chen Lin, visiting student, collaborating with Nvidia Research and the National Tsing Hua University in Taiwan) was presented by Lin at the conference workshop on Machine Deception.

Raisi and Yao presented posters at the Women in Machine Learning Workshop. Raisi presented “A Weakly Supervised Deep Model for Cyberbullying Detection” (Elaheh Raisi, Bert Huang); and Yao presented “Fairness and Accuracy in Recommendation with Imbalanced Data Sparsity” (Sirui Yao, Bert Huang).


The Discovery Analytics Center enhances strengths with four new faculty

Left to Right (top), Mark Embree, Tanushree Mitra; (bottom) Srijan Sengupta, Jia-Bin Huang

The Discovery Analytics Center welcomes four new faculty this fall who will help lead Virginia Tech’s big data research and education efforts on campus.

“Data analytics is inherently interdisciplinary and our new faculty bring expertise that will bolster our strengths in matrix computations, statistical methodology for network data, computer vision, and information credibility as we strive to find data solutions to modern problems,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science and director of the Discovery Analytics Center.

The center has become a well-recognized force among the analytics community within the commonwealth and beyond and fosters multi-stakeholder collaborations with fellow universities, leading industry affiliates, government agencies, and nonprofit organizations. Officially housed within the Computer Science Department, faculty and graduate students represent computer science, statistics, electrical and computer engineering, and math.

The four new faculty are: Mark Embree, professor of mathematics and associate director of the Virginia Tech Smart Infrastructure Laboratory; Jia-Bin Huang, assistant professor of electrical and computer engineering; Tanushree (Tanu) Mitra, assistant professor of computer science; and Srijan Sengupta, assistant professor of statistics.

A Virginia Tech alumnus, Mark Embree received a bachelor’s degree in computer science and mathematics in 1996. He earned a doctor of philosophy degree in numerical analysis from Oxford University, where he was a Rhodes Scholar, and taught at Rice University from 2001 to 2013. In 2014, he returned to Virginia Tech in Blacksburg to lead the Computational Modeling and Data Analytics program in the College of Science Academy of Integrated Science.

Embree’s research interests include numerical analysis, especially matrix computations; data analytics for smart buildings; dynamics and perturbation theory for non-self-adjoint operators; and spectral theory for Schrödinger operators.

He has authored numerous papers and technical reports and is coauthor of “Spectra and Pseudospectra: The Behavior of Nonnormal Matrices and Operators,” published by Princeton University Press.

Jia-Bin Huang, joined Virginia Tech in 2016. He earned a bachelor’s degree in electronics engineering from National Chiao-Tung University in Taiwan and a Ph.D. in electrical and computer engineering at the University of Illinois at Urbana-Champaign.

In 2014, Huang received the best paper award at the Association for Computing Machinery Symposium on Eye Tracking Research and Applications. In 2012, he received the best student paper award at the International Association for Pattern Recognition conference for his work on computational modeling of visual saliency.

His research interests include computer vision; computer graphics; and machine learning with a focus on visual analysis and synthesis with physically grounded constraints.

Tanushree (Tanu) Mitra joined Virginia Tech after earning a Ph.D. in computer science from the Georgia Institute of Technology in August 2017, where the GVU Center named her a Foley Scholar, the highest award for student excellence in research contributions to computing.

She was an IBM Ph.D. Fellowship Recipient in 2016 and selected to attend the Consortium for the Science of Socio-Technical Systems, a National Science Foundation-funded workshop for promising junior investigators.

Mitra earned a master’s degree in computer science from Texas A&M University and a bachelor’s degree in computer engineering from Sikkim Manipal Institute of Technology in India. Her internships included IBM Research and Microsoft Research.

Mitra’s research combines computational techniques and social science principles to study complex social processes underlying human behavior in large-scale online social systems. Specific topics of focus include social computing; computational social science; social media content analysis; data mining; credibility perceptions; misinformation and deception; online communities; and quantitative and qualitative data analysis.

Srijan Sengupta joined Virginia Tech in 2016 as assistant professor of statistics after earning a Ph.D. in statistics from the University of Illinois at Urbana-Champaign. For his dissertation, “Statistical analysis of networks with community structure and bootstrap methods for big data,” Sengupta was awarded the university’s Norton Prize for Outstanding Ph.D. Thesis.

Sengupta received both a bachelor’s and master’s degree in statistics, both with first class distinction, from the Indian Statistical Institute.

His research interests are primarily in statistical methodology for network data; bootstrap and related resampling methods; big data; and computational statistics. Sengupta is also interested in statistical applications in wide-ranging problems in science and industry.


DAC has strong presence at ICDM 2017

DAC Ph.D. student, Zhiqian Chen, presenting his paper at ICDM 2017.

The Discovery Analytics Center was strongly represented at the IEEE International Conference on Data Mining (ICDM) in New Orleans, Nov. 18-21, with a number of accepted research papers by DAC faculty and students and DAC faculty serving on committees and panels.

Research papers accepted for the conference include:

DAC faculty participation in the ICDM Conference included Chang-Tien Lu serving on the program committee and Naren Ramakrishnan serving as an area chair. Ramakrishnan also co-chaired a panel focusing on ethical and professional issues when dealing with social data with Tanushree (Tanu) Mitra, assistant professor of computer science, as one of the panelists. B. Aditya Prakash was invited to participate as a mentor in the ICDM Ph.D. Forum.

The ICDM has established itself as the world’s premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of data mining, including algorithms, software and systems, and applications. ICDM draws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high-performance computing. By promoting novel, high quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to continuously advance the state-of-the-art in data mining. Besides the technical program, the conference features workshops, tutorials, panels.

 

 

 

 


Brian Goode recognized with Innovation Award from the Fragile Families Challenge

Brian Goode focused on data-driven and process-driven approaches to create predictive models for six outcomes of 4,242 participants. He presented his work at the Fragile Families Challenge Scientific Workshop at Princeton University last week. Click here to read more about Brian’s award.


Elaheh Raisi and Bert Huang awarded ACM/IEEE Best Paper Award at Sydney conference

Elaheh Raisi, a computer science Ph.D. student in the Discovery Analytics Center and her advisor, Bert Huang, assistant professor in the Department of Computer Science, were recently honored with the Best Paper Award at the 2017 IEEE/Association for Computing Machinery International Conference on Advances in Social Networks Analysis and Mining (ASONAM), in Sydney, Australia.

In the paper, entitled “Cyberbullying Detection with Weakly Supervised Machine Learning,” Raisi and Huang propose a machine learning method for simultaneously inferring user roles in harassment-based bullying and new vocabulary indicators of bullying. The learning algorithm considers social structure and infers which users tend to bully and which tend to be victimized. The model estimates whether each social interaction is bullying based on who participates and based on what language is used, and it tries to maximize the agreement between these estimates. The two researchers then evaluate participant vocabulary consistency on three social media data sets, demonstrating quantitatively and qualitatively its effectiveness in cyberbullying detection.

Raisi works at the Machine Learning Lab. Her research interests include machine learning, data mining, and social networks.

Huang’s research investigates machine learning, with a focus on analyzing complex systems. His work addresses topics including structured prediction, probabilistic graphical models, and computational social science.

The international conference on Advances in Social Network Analysis and Mining provides an interdisciplinary venue that brings together practitioners and researchers from a variety of SNAM fields to promote collaborations and exchange of ideas and practices. The 2017 conference addressed important aspects with a specific focus on the emerging trends and industry needs associated with social networking analysis and mining and solicited experimental and theoretical works on social network analysis and mining along with their application to real life situations.

Full papers were reviewed and assessed by the program committee to determine the “Best Paper Award” winner.


Center for American Progress report cites Discovery Analytics Center collaboration with commonwealth of Virginia as example of improving workforce data

People walk through the Oculus at the World Trade Center in New York, June 16, 2017.

A Center for American Progress report on using open data standards to enhance the quality and availability of online job postings has highlighted the Gov. Terry McAuliffe’s Commonwealth Consortium for Advanced Research and Statistics (CCARS) and its work with the Discovery Analytics Center at Virginia Tech to develop the Open Data, Open Jobs Initiative. The goal of the pilot was to capture and publish a real-time structured data feed of all online job postings in Virginia that would serve as a proof of concept.

The dataset was created in large part by Ph.D. student Rupinder Paul Khandpur, who was also in the governor’s data internship program.

Read the Center for American Progress report here.


DAC Ph.D. student Rupinder Paul Khandpur invited to speak at CyCon

 Rupinder Paul Khandpur, a DAC Ph.D student in computer science, was invited to speak to a group of analysts at the 2017 International Conference on Cyber Conflict (CyCon). The conference, held in Tallinn, Estonia, focused on the fundamental aspects of cyber security with a theme of Defending the Core.

Khandpur’s presentation discussed how to use open source indicators such as Twitter to rank both cyber and physical threats.

Khandpur’s research concentrates on applied data sciences with an emphasis on event forecasting, threat analytics, and narrative generation using open source data. He was part of the team working on EMBERS, an IARPA OSI (Open Source Indicators) project aimed at forecasting significant societal events (disease outbreaks, civil unrest, elections) from open source datasets. He earned a master’s degree in computational biology from Carnegie Mellon University.

CyCon is organized by the NATO Cooperative Cyber Defence Centre of Excellence. Every year, more than 500 decision-makers and experts from government, military, and industry from all over the world approach the conference’s key theme from legal, technology, and strategy perspectives, often in an interdisciplinary manner.


DAC and BI lead DARPA’s Next Generation Social Science Project

brian & Chris

Brian Goode (left), from the Discovery Analytics Center, and Chris Kuhlman, from the Biocomplexity Institute at Virginia Tech, collaborate on developing models for large-scale social behavior.

DAC and the Biocomplexity Institute are leading a $3 million grant awarded by the Defense Advanced Research Projects Agency (DARPA) as part of the Next Generation Social Science (NGS2) program.  DAC and BI will conduct research that will streamline modeling processes, experimental design, and methodology in the social sciences. A major objective of the project is to make social science experiments rigorous, reproducible, and scalable to large populations.


Graduate certificate in urban computing approved

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Left to right: Hesham Rakha and Huthaifa Ashqar work on a simulation of speed harmonization algorithm on I-66 using INTEGRATION; Scotland Leman and Matt Slifko discuss spatial relationships in the housing market.

New interdisciplinary certificate in urban computing, part of National Science Foundation (NSF) Research Traineeship UrbComp Program, is now available to all Virginia Tech graduate students. Administered through the Discovery Analytics Center, the 12-credit certificate program weaves interdisciplinary applications through new courses and a novel “tapestry” curriculum.

These courses are designed to train students to become competent problem solvers by developing computational models of urban populations from disparate data sources and posing and answering what-if questions via machine learning and visualization methodologies. Students are also trained in the ethical and professional implications of working with massive datasets.  Click here to read more about the certificate.


DAC Director Naren Ramakrishnan explores big data analytics to plan for smart cities

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Naren Ramakrishnan, DAC director and professor of computer science.

DAC director, Naren Ramakrishnan, takes part in a VT Engineering team leading a three-year, $1.4 million National Science Foundation (NSF) grant to develop a new planning framework for smart, connected, and sustainable communities.  The team wants smart cities to features zero energy, zero outage, and zero congestion.  They are utilizing big data and interdisciplinary technology as tools to meet that goal.  Click here to read more about the project.


Coverage of DAC Ph.D. student Yaser Keneshloo’s research with Washington Post

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The summation chain around pulleys on Tide Predicting Machine No. 2.

Great coverage of DAC Ph.D. student Yaser Keneshloo’s research in collaboration with the Washington Post on applying data science to predict the popularity of news articles.  Keneshloo and the Post are working on a popularity prediction experiment, they are doing clickstream analysis and producing a pipeline for processing tens of millions of daily clicks, for thousands of articles. Click here to read more about Keneshloo’s project.

 

 


DAC faculty Chandan Reddy wins Best Student Paper at IEEE

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Chandan Reddy (left) and his collaborators from the the Korea University (right).

Congratulations to Chandan Reddy, DAC faculty member and associate professor of Virginia Tech – Computer Science, whose paper in collaboration with Korea University, Boosted L-EnsNMF: Local Topic Discovery via Ensemble of Nonnegative Matrix Factorization, received the Best Student Paper Award at the IEEE Conference on Data Mining! Click here for a full list of awards.


DAC PhD student Saurav Ghosh published in Nature Scientific Reports

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Flow chart depicting the sequential modeling process of EpiNews

DAC PhD student Saurav Ghosh’s work was published in Nature Scientific Reports. His research explores relationships between news coverage and modeling of infectious disease outbreaks

The research is in collaboration with Boston Children’s Hospital and University of Washington, Seattle. Click here to read more about Ghosh’s research.


DAC director Naren Ramakrishnan receives grant from Army Research Lab

ece_article_161221_internet_of_battlefield_articleWalid Saad, assistant professor in electrical and computer engineering, and Naren Ramakrishnan, and professor of computer science and director of DAC, are leading a $324,000 U.S. Army Research Laboratory grant that is laying groundwork for the Internet of Battlefield Things.

They are developing a planning framework that would present mathematical tools to understand how to transform existing battlefield capabilities into a large-scale IoBT. Click here to read more about the project.


DAC recognized for project in workforce analytics

wanawsha & rupen

Left to right at the Governor’s Workforce Innovation Challenge Datathon 2016 are computer science Ph.D. student Rupinder Paul Khandpur; Virginia Secretary of Technology Karen Jackson; and Wanawsha Hawrami, manager of operations for DAC.

DAC has been recognized for its contributions in a project focused on workforce analytics for Governor Terry McAuliffe’s Open Data, Open Jobs portal.  DAC is playing a key role in the governor’s commitment to improving the labor market in Virginia.

Open Data, Open Jobs is a real-time curation, analysis, and visualization of advertised job postings in Virginia. All curated jobs are published on the DAC’s open data portal, accessible through a publicly available API in machine-readable format, with a unified job posting schema that eliminates the need to navigate separate public and private listings dispersed across multiple sites, such as Monster or LinkedIn.

DAC was on-board from the onset, providing necessary support to harvest, clean, and enrich individual datasets to create the new workforce data product. The dataset was created in large part by DAC Ph.D. student, Rupinder Paul Khandpur, who was also in the governor’s data internship program. Click here to read more about the Open Data, Open Jobs project.


DAC faculty Ed Fox awarded new grant from NSF

Discovery Analytics Center

Ed Fox (right) and his Ph.D. students (left).

Ed Fox, DAC faculty member and professor of computer science, takes part in Coordinated, Behaviorally-Aware Recovery for Transportation and Power Disruptions project which was just awarded a Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP) Award from the National Science Foundation (NSF). The grant is to study behavioral adaptation during disruptive events affecting power and transportation. Click here to read more about the project.


DAC Director Naren Ramakrishnan named Inventor of the Month

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Members of the staff of the Discovery Analytics Center. Left to right are Nathan Self, Patrick Butler, and Naren Ramakrishnan.

DAC and director, Naren Ramakrishnan, are featured as this month’s Virginia Tech​ Inventors of the Month by the Office of Research and Innovation for work in Early Model Based Event Recognition using Surrogates (EMBERS) software project.

EMBERS is a fully automated system for forecasting significant societal events, such as influenza-like illness case counts, rare disease outbreaks, civil unrest, domestic political crises, and elections, from open source surrogates. To read more about EMBERS click here.


DAC Alumna Jessica Self raising diversity awareness

selfJessica Zeitz Self, DAC Ph.D. alumna who was was advised by Dr. Chris North, professor of Virginia Tech – Computer Science and associate director of DAC, discusses her experiences at Virginia Tech that allowed her to help decrease the gender gap of women in the field of computer science.

Self became a champion for diversity through efforts such as Women in Computing Day, an event that brings seventh-grade girls to Virginia Tech to learn about computer science in nontraditional ways. Click here to read more about Self’s work.


Liang Zhao named one of Top 20 New Stars in Data Mining

nvc-3Congratulations to Liang Zhao, a recent DAC Ph.D. graduate in computer science, who has been named one of the Top 20 New Starts in Data Mining, provided by Microsoft searching. Liang was advised by Chang-Tien Lu, associate director of DAC and professor of computer science.

Microsoft searching mines the past six years of Knowledge Discovery and Data Mining (KDD) submissions and combines the big data from Microsoft to then achieve the ranking by an automatic algorithm. KDD is the top conference in the data mining area. Click here if you’d like to read more.

 


Scotland Leman receives W.J. Youden Award

Scotland lemanCongratulations to Scotland Leman, DAC faculty member and associate professor in the department of statistics, on receiving the W.J. Youden Award in Interlaboratory Testing. Dr. Leman was presented with the award at the 2016 Fall American Statistical Association Technical Conference. The award recognizes the authors of publications that make outstanding contributions to the design and/or analysis of interlaboratory tests or describe ingenious approaching to the planning and evaluation of data from such tests.  Click here to read more about the award.


DAC collaborating with General Dynamics Mission Systems

Discovery Analytics Center

Computer science professor Chris North, left, with Ph.D. student, Caleb Reach at DAC’s InfoVis Lab in Torgersen Hall.

DAC is collaborating with General Dynamics Mission Systems on an exciting venture that will help intelligence analysts find important information more quickly.  Chris North, associate director of DAC and professor of computer science, is leading the collaboration from the university side. North’s research group is developing a “smart” software that uses a visual interface and machine learning algorithms to allow the analyst’s interactions with the data to guide future searches. To read more about the partnership click here.


Chandan Reddy receives grant from NSF

user_interest_model[1]Congratulations to Chandan Reddy, our new DAC faculty member and associate professor of computer science for receiving an award from the National Science Foundation for his project EAGER: An Integrated Predictive Modeling Framework for Crowdfunding Environments.  

EAGER aims to study data analytics tools for improving crowdfunding project success rate. Crowdfunding provides seed capital for start-up companies, creating job opportunities and reviving lost business ventures. In spite of the widespread popularity and innovativeness in the concept of crowdfunding, however, many projects are still not able to succeed. A deeper understanding of the factors affecting investment decisions will not only give better success rate to the future projects but will also provide appropriate guidelines for project creators who will be seeking funding.  Click here to read more about Chandan’s project.


DAC helps prepare for Governor’s Workforce Innovation Challenge

DAC Ph.D. student Rupinder Paul Khandpur (left) and Manager of Operations Wanawsha Hawrami (far right) with Karen Jackson, Secretary of Technology for the Commonwealth of Virginia.

DAC Ph.D. student Rupinder Paul Khandpur (left) and DAC Manager of Operations Wanawsha Hawrami (far right) with Karen Jackson, Secretary of Technology for the Commonwealth of Virginia.

DAC Ph.D. student Rupinder Paul Khandpur (right) explaining the Open Jobs datasets he prepared for the Governor's Workforce Innovation Challenge.

DAC Ph.D. student Rupinder Paul Khandpur (right) explaining the Open Jobs datasets he prepared for the Governor’s Workforce Innovation Challenge.

As part of DAC’s continued involvement in the Open Data, Open Jobs Initiative, we have collaborated with the Governor’s office in preparing for the Workforce Innovation Challenge held on Aug. 25 – 26.  The datathon is a part of the Governor’s New Virginia Economy initiative. The innovations expected to come out of the datathon will help the commonwealth gain a deeper understanding of the current and future job opportunities in today’s new economy.  DAC Ph.D student played a crucial role in preparing and harvesting the Open Jobs datasets used by participants in the datathon to develop apps. Click here to learn more about the datathon.  


Edward Fox receives XCaliber Award

Edward Fox and his Ph.D. students at the DAC lab in Torgersen Hall.

Edward Fox and his Ph.D. students at the DAC lab in Torgersen Hall.

Congratulations to Edward Fox, DAC faculty member and professor in the department of computer science on receiving Virginia Tech’s 2016 XCaliber Award.  Edward is being recognized for his extraordinary contributions to technology enriched active learning.  More specifically for his new computer science courses, CS 4984, Computational Linguistics and CS 5604, Informational Retrieval.  The XCaliber Award is given to faculty and staff who integrate technology in teaching and learning, celebrating innovative and student-centered teaching.  Click here to read more about the award.


DAC welcomes new faculty member, Chandan Reddy

reddy1-updatedDAC welcomes our new faculty member, Chandan Reddy, who was appointed to associate professor in the Department of Computer Science.  Chandan is joining us from Wayne State University where he was the director of the Data Mining and Knowledge Discovery (DMKD) Laboratory.  His primary research interests are data mining and machine learning with applications to healthcare analytics, social network analysis and bioinformatics.  Chandan is joined by two Ph.D. students, Ping Wang and Tian Shi.  Click here to learn more about Chandan.


Gov. Terry McAuliffe highlights DAC’s work in Open Data, Open Jobs initiative

Map showing geographical distribution of job postings in Virginia, featured on opendata.cs.vt.edu

Map showing geographical distribution of job postings in Virginia, featured on opendata.cs.vt.edu.

Governor of Virginia Terry McAuliffe’s office has sent out a press release announcing Open Data, Open Jobs; a groundbreaking data analytics initiative to better connect job seekers to job opportunities.  DAC Ph.D. student Rupinder Paul Khandpur has been working on this project via the Governor’s Big Data Internship Program (GDIP), a part of the Governor’s New Virginia Economy Workforce Initiative.  The project is an initiative of the Commonwealth Center for Advanced Research and Statistics (CCARs), a virtual center for modeling innovation approaches for improving and using labor market, workforce, and education data. To read more about Open Data, Open Jobs click here.


Congratulations to our 2016 DAC Graduates!

graduations copyAs the dust settles from graduation, DAC would like to recognize the students who have graduated this year.  DAC is proud to have had eight graduate students complete their degrees this spring semester; seven of which received a Ph.D. and one receiving a Master’s of Science. Below we highlight our students who are now prepared to assume roles as faculty members, researchers, and data analysts. We look forward to their contribution to the field data science and cannot wait to see what they achieve from here. Congratulations!

Harsh Agrawal received a Master’s of Science in Electrical and Computer Engineering.  His thesis was titled ‘CloudCV: Deep Learning and Computer Vision on the Cloud.’ His research focuses on problems at the intersection of computer vision and machine learning.  Harsh built CloudCV which is a large scale cloud system with the aim to democratize computer vision and deep learning algorithms and make it accessible to anybody who wants to apply computer vision to their research or software applications. He will now be joining Snapchat as a research engineer where he hopes to apply computer vision and deep learning to build the next generation mobile communication app.

Marcos Carzolio received a Ph.D. in Statistics. His research is on a selection of Markov chain Monte Carlo methods for large scale inference and big data. Specifically, he is developing a new algorithm called weighted particle tempering, and applying it and another algorithm called reversible jump Markov chain Monte Carlo to average over free B-spline models for a dataset about child development in rural Mozambique. Marcos will be working at Goldman Sachs Asset Management in New York City as a strategist.

Pritwish Chakroborty received a Ph.D. in Computer Science.  His thesis focused on formalizing disease forecasting models using open source indicators.  Disease surveillance is often delayed an unstable; however, real time information about diseases could be obtained from sources such as news and weather. Pritwish built a number of statistical models borrowing principles from GLM, MCMC and Matrix Factorization methods to build forecasting models for endemic diseases such as Flu and CHIKV.  He also built and managed the endemic disease forecasting framework which was used to send continuous forecasts to IARPA and CDC. Pritwish will be joining IBM Watson Health, USA where he will shift focus to more micro level disease models towards personal health.

Andy Hoegh received a Ph.D. in Statistics.  His dissertation research focused on statistical algorithms for fusing predictions from a set of models with the primary goal of predicting instances of civil unrest. In the fall he will be starting as an assistant professor of statistics in the Department of Mathematical Sciences at Montana State University.

Fang Jin received a Ph.D. in Computer Science. Her dissertation is about mass movements and their adoptions in social media. Her work includes how to capture mass movements diffusion patterns across a wide geographical area, how to detect events based on group anomalies, how to distinguish real movements from rumors, etc.

Marjan Momtazpour received a Ph.D. in Computer Science.  Her thesis was titled the ‘Knowledge Discovery for Sustainable Urban Mobility’. She has published several papers in the areas of Energy Management, Urban Infrastructure Investment, Anomaly Detection in urban transportation, and Outlier Detection in time series of general cyber-physical systems. She plans to join Microsoft  Data Platform group located in Redmond,WA.

Jessica Zeitz Self received a Ph.D. in Computer Science. Her dissertation focused on designing and evaluating object-level interaction to support human-model communication in data analysis. She is joining the Computer Science Department at the University of Mary Washington as an Assistant Professor this fall.

Maoyuan Sun received a Ph.D. in Computer Science.  His research interests include Visual Analytics, Information Visualization, Human Computer Interaction, Human Centered Machine Learning and Usable Security. In his Ph.D. dissertation, Maoyuan explores the design space of bicluster visualizations to support coordinated relationship exploration. Maoyuan has accepted a tenure-track faculty position offer from the University of Massachusetts Dartmouth.  He will start working as an assistant professor in the Computer & Information Science Department, College of Engineering this coming fall.

 

 


Chang-Tien Lu promoted to professor

CT LuCongratulations to DAC associate director, Chang-Tien Lu, who has been promoted to full professor in the department of computer science.  Dr. Lu is an ACM Distinguished Scientist.  His research focuses on data management to fulfill emerging requirements for storing, analyzing, and visualizing spatial data. To read more about this years promotions, click here.


Edward Fox and Virginia Tech researchers earn grant to study big data sharing and reuse

Discovery Analytics Center

Edward Fox (right) and DAC students.

Congratulations to Edward Fox, professor of computer science and DAC faculty member, who is among a group of Virginia Tech researchers collaborating with Virginia Tech Libraries that has recently been awarded a $308, 175 National Leadership Grant for Libraries from the Institute of Museum and Library Services.  The team will be exploring effective ways of storing and reusing bid data.

 

“The IMLS grant will allow contrasting use of the cloud with local infrastructures, like ours that is tailored for integrating focused crawling from the web, tweet collection, collaboration with the Internet Archive, and advanced methods of machine learning, natural language processing, information retrieval, digital libraries, archiving, visualization, and human-computer interaction,” said Fox. To learn more about the grant click here.


Bert Huang presents at CCC Symposium

PastedGraphic-1Bert Huang, DAC faculty member and assistant professor of computer science, presented a poster at the Computer Computing Consortium Symposium on Addressing National Priorities and Societal Needs.  Dr. Huang presented his research on machine learning for cyberbullying, specifically weakly supervised cyberbullying detecting in social media. Click here to watch a video of his presentation.

 


DAC now offering a new graduate certificate in data analytics

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Left to right, computer science Professor Chris North explains dimensionality reduction methods for interactive visual text analytics to Ph.D. students Jessica Self and Maoyuan Sun. This is one of the topics covered for the new graduate certificate in data analytics.

DAC is proud to announce that we will now be offering a new graduate certificate in data analytics.  The certificate is offered collaboratively by Virginia Tech’s departments of computer science, statistics, and electrical and computer engineering.  The 12-credit program will be open to students both in Blacksburg and the National Capital Region.  It will better prepare students for careers in data analytics and data science, one of the nation’s fastest growing fields.  For more information about our certificate in data analytics, click here.


DAC Director Naren Ramakrishnan gives keynote talk at Pacific Asia Knowledge Discovery and Data Mining Conference

DAC Director Naren Ramakrishnan at PAKDD 2016.

DAC Director Naren Ramakrishnan at PAKDD 2016.

DAC Director Naren Ramakrishnan gave the opening keynote talk at the Pacific Asia Knowledge Discovery and Data Mining Conference on April 20, which was held in Auckland, New Zealand this year.  Dr. Ramakrishnan provided overview and perspectives about DAC’s EMBERS project aimed at a data mining audience. To learn more about the conference, click here.


DAC Director Naren Ramakrishnan edits IEEE Computer’s magazine

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Cover of IEEE Computer’s April 2016 issue

DAC Director Naren Ramakrishnan guest edits IEEE Computer’s April 2016 issue, which is focused on Big Data.  Dr. Ramakrishnan guested edited along with Ravi Kumar from Google.  Read the issue to explore the latest in databases, algorithms, and applications of big data here.


DAC takes part in study expected to measure region’s growth in entrepreneurship

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Khaled Hussein is co-founder and chief technology officer of California-based technology company Tilt, which opened an office in Blacksburg last year. Hussein and seven other employees are Virginia Tech alums.

DAC, in collaboration with Virginia Tech’s Office of Economic Development, is taking part in an important study to measure the Roanoke and Blacksburg region’s growth in entrepreneurship. DAC will provide an analysis of entrepreneurs’ social-media use in the hopes of promoting jobs and entrepreneurship in the region. To read more about the study, click here.


DAC’s Brian Goode judges Northern Virginia Science and Engineering Fair

brian-updatedDAC was happy to participate again this year at the local science and engineering fairs. Brian Goode, DAC research scientist, served as a judge at the Northern Virginia Science and Engineering Fair at Wakefield High School in Arlington. To read more about Virginia Tech’s involvement in the science fair, click here.


Devi Parikh and Dhruv Batra discuss artificial intelligence on WVTF Public Radio

Demonstration on of VQA project.

Demonstration on of VQA project.

DAC faculty members Devi Parikh and Dhruv Batra interview wit WVTF Public Radio and RADIO IQ to discuss their leading efforts in the artificial intelligence community. Parikh and Batra shared insight into their Visual Question and Answering (VQA) project, which tackles the next frontier in artificial intelligence, which is teaching computers to ‘see,’ that is, to recognize unique objects the way humans do. To hear Parikh and Batra’s interview, click here.

 

 

 


DAC’s Aditya Prakash co-authored a book titled “The Global Cyber-Vulnerability Report”

Prakash-updatedDAC faculty member, Aditya Prakash has co-authored a book titled “The Global Cyber-Vulnerability Report,” in collaboration with the University of Maryland Institute for Advanced Computer Studies.

This book establishes metrics to measure cyber-vulnerability of countries and quantify the cyber-vulnerability of countries. In addition, it offers useful data-driven policy advice for law-makers and policy-makers in each country. It is also the first that uses cyber-vulnerability data to explore the vulnerability of over four million machines per year, covering a two-year period as reported by Symantec. Analyzing more than 20 billion telemetry reports comprising malware and binary reputation reports, this book quantifies the cyber-vulnerability of 44 countries for which at least 500 hosts were monitored.

Click here for more info about “The Global Cyber-Vulnerability Report.”


Devi Parikh receives the Office of Naval Research Young Investigators Award

Devi ParikhDevi Parikh, DAC faculty member and assistant professor of the department of electrical and computer engineering received the Office of Naval Research Young Investigators Award, one of the oldest and most selective scientific research advancement programs in the country!

Parikh is being recognized for her exceptionally creative research which holds promise across a range of naval-relevant science and technology areas. Click here to read more about her award.

 


DAC Associate Director Chris North Awarded a Grant from Microsoft

Discovery Analytics Center

Chris North with DAC Ph.D. students from the InfoVis Lab.

DAC associate director, Chris North, along with other Virginia Tech researchers led by Joseph Gabbard, associate professor in the Department of Industrial and Systems Engineering, received a grant from Microsoft for the amount of $100,000.  The grant will be used to explore the potential uses of its HoloLens devices for advancing research in the area of mixed reality and the possibilities of holographic computing. The team of researchers includes faculty from theInstitute for Creativity, Arts, and Technology and the Center for Human-Computer Interaction.  To read more about this grant click here.


DAC’s collaboration with the Washington Post gets noticed

Yaser_Keneshloo-updatedThe Washington Post director for Big Data and Personalization, Sam Han, discussed the Post’s collaboration with DAC at the Predictive Analytics Innovation Summit in San Diego this past weekend.  Yaser Keneshloo, DAC Ph.D. student, has been working with the Post on improving user experience by predicting the popularity of a news article.  His work allows editors to prioritize stories, identify under-performing articles for content variable testing, and supports advertising opportunities.  To read more about Sam Han’s presentation click here


Devi Parikh receives NSF CAREER Award

Devi ParikhDevi Parikh, DAC faculty member and assistant professor in the department of electrical and computer engineering received a National Science Foundation (NSF) Faculty Early Career Development (CAREER) award for her Visual Question Answering (VQA) research, a system of using images to teach a computer to respond to any question that might be asked. The CAREER grant is NSF’s most prestigious award, given to junior faculty members who are expected to become academic leaders in their field.  To read more about Parikh’s award click here.


Devi Parikh and Dhruv Batra’s Work in AI Featured in Newsweek

devi_dhruv-300x222

Dhruv Batra (left) and Devi Parikh (right) are developing Visual Question Answering Capability for computers. Visual machine perception requires powerful computation capability. The team shares 500- core CPU cluster, each an order of magnitude more powerful than a laptop, and a GPU cluster.

DAC faculty members and assistant professors of ECE, Devi Parikh and Dhruv Batra’s project on Learning Common Sense through Visual Abstractions was featured in Newsweek. The article focuses on an artificial intelligence algorithm they trained to understand and predict visual humor, representing a major development towards creating “common sense” machines.  Read more about Devi and Dhruv’s algorithm here.


Chang-Tien Lu Named ACM Distinguished Scientist

ctlu

Chang-Tein Lu, Associate Director of DAC and Associate Professor of Computer Science became an Association for Computing Machinery Distinguished Scientist.  ACM is the world’s leading association of computing professionals. As a distinguished member, Chang-Tein, is recognized as an innovative leader in the field of computing.  To read more about ACM click here.

 


Chang-Tien Lu leads Virginia Tech in NSF Big Data Innovation Hub

Virginia Tech graduate students use a display wall in the Discovery Analytics Center to view epidemiological simulations of disease outbreaks in a city, one of the many big data applications that will be studied in the Big Data Innovation Hub.

Virginia Tech graduate students use a display wall in the Discovery Analytics Center to view epidemiological simulations of disease outbreaks in a city, one of the many big data applications that will be studied in the Big Data Innovation Hub.

Chang-Tien Lu, associate professor of computer science and associate director of DAC is leading Virginia Tech as it takes part in a multi-university effort to apply big data solutions to regional challenges. Chang-Tien will be playing a vital role in the university’s broad-base collaboration on the project, an initiative supported by the National Science Foundation that brings together research universities across the south to develop a Big Data Regional Innovation Hub.  Read more about Chang-Tien’s part in this project here.


Kurt Luther and Chris North awarded NSF Grant

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Kurt Luther (left), Chris North (right)

Chris North, professor of computer science and associate director of DAC, and Kurt Luther, assistant professor of computer science were awarded a $500,000 grant from NSF over three years from its cyber-human system program area.  The grant focuses on using crowdsourcing to help analyze big data and solve problems. Crowdsourcing, in this sense, means soliciting contributions of data from a large group of people, most of whom are online users. To read more about Kurt and Chris’s project click here.


Lenwood Heath receives NSF PIRE Award

LennyHeath_06_2003_50

Lenwood Heath, a professor of computer science and faculty member of DAC is of a part group of faculty members at Virginia Tech awarded a five-year $3.6 million Partnerships in International Research and Education (PIRE) grant from the National Science Foundation (NSF) that is aimed at mitigating the global threat of antibiotic resistance spread through the contact or consumption of contaminated water.  Disease free water is a global health challenge that commands an international team effort.  To read more about this project click here.


NSF funds UrbComp, program focused on big data and urbanization

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DAC will create and administer a new interdisciplinary Ph.D. certificate program called UrbComp, which is set to launch in spring 2016.  The UrbComp Ph.D. certificate is focused on big data and urbanization through a $3 grant over five years from the National Science Foundation Research Traineeship Program. UrbComp will be open to students from both the Blackburg and National Capital Region campuses who are pursuing a Ph.D. in one of eight departments: computer science, mathematics, statistics, electrical and computer engineering, population health sciences, urban affairs and planning, civil and environmental engineering, or sociology. To read more about the program click here.


Aditya Prakash works on collaborative project about the Russian flu epidemic

Aditya Prakash (left), Amy Nelson, and Tom Ewing are collaborators on the Russian flu project.

Aditya Prakash (left), Amy Nelson, and Tom Ewing are collaborators on the Russian flu project.

DAC faculty member Aditya Prakash, an assistant professor in the department of computer science is working on a multi-disciplinary project about the Russian flu epidemic of the late 19th century.  He is working with faculty in the department of history, specifically professor Tom Ewing and associate professor Amy Nelson.  They have received a $175,000 grant from the National Endowment for the Humanities (NEH) for their research and are collaborating with the Leibniz Universität Hannover in Germany t0 examine medical discussion and news reporting during the epidemic.  To read more about this project click here.


DAC faculty member Ravi Tandon receives tenure-track assistant professorship

Ravi_newsResearch assistant professor Ravi Tandon has joined the University of Arizona on a tenure-track assistant professorship. Congratulations to Ravi! While at DAC, his research focused on information-theory based approaches to data analytics and forecasting. He participated in the IARPA-supported EMBERS project where he developed new quickest event detection and social media analytics approaches. DAC bids him a fond farewell with best wishes for his career! Read more


Devi Parikh and Dhruv Batra receive another Google Research Award

Dhruv Batra (left) and Devi Parikh (right) are developing Visual Question Answering Capability for computers. Visual machine perception requires powerful computation capability. The team shares 500- core CPU cluster, each an order of magnitude more powerful than a laptop, and a GPU cluster.

Dhruv Batra (left) and Devi Parikh (right) are developing Visual Question Answering Capability for computers. Visual machine perception requires powerful computation capability. The team shares 500- core CPU cluster, each an order of magnitude more powerful than a laptop, and a GPU cluster.

Devi Parikh and Dhruv Batra, DAC faculty members and assistant professors of electrical and computer engineering have received another Google Research Award in the amount of $92,000 for their Visual Question Answering (VQA) project. This is Parikh’s third Google Research grant, and Batra’s second. The grant will be to develop a new approach in teaching computers to understand images with the goal of enabling the computer to provide a natural-language answer to a specific question.  To read more about the grant click here.

 


Aditya Prakash receives one of only ten Facebook Faculty Award

badityap-portraitCongratulations to Aditya Prakash on his Facebook Faculty Award, one of only 10 such awards given this year! The award will support novel information diffusion related research focusing on understanding, predicting and countering virality on social-media websites and platforms. For example, some of the questions Aditya will study include: “What content could go viral? How much and when? Given a context, how to identify and counter negative viral campaigns?” Look forward to exciting results from this research!


DAC/CS PhD student Saurav Ghosh wins best paper award at SIAM Data Mining 2015

myselfCongratulations to Saurav Ghosh! The DAC/CS Ph.D. student co-authored SourceSeer: Forecasting Rare Disease Outbreaks Using Multiple Data Sources“, which garnered the Best Paper Award at the SIAM International Conference on Data Mining held in Vancouver, Canada.

The study described in the paper was led by Theodoros Rekatsinas, a Ph.D. student in the Department of Computer Science, University of Maryland, College Park. In addition to Rekatsinas and Ghosh, the other authors of the paper include Sumiko R. Mekaru, Children’s Hospital Informatics Program, Boston Children’s Hospital, Boston, Massachusetts; Elaine O. Nsoesie and John S. Brownstein, Children’s Hospital Informatics Program, Boston Children’s Hospital and Department of Pediatrics, Harvard Medical School, Boston, Massachusetts; Lise Getoor, Professor of Computer Science, University of California Santa Cruz; and Naren Ramakrishnan, Thomas L. Phillips Professor of Engineering and director, Discovery Analytics Center, Department of Computer Science, Virginia Tech. Read the Virginia Tcch news release.


VQA Project Featured in Bloomberg Business

DAC Assistant Professors Dhruv Batra and Devi Parikh discuss their Visual Question Answering (VQA) project with students from thier Computer Vision Lab

DAC Assistant Professors Dhruv Batra and Devi Parikh discuss their Visual Question Answering (VQA) project with students from thier Computer Vision Lab

DAC faculty members and assistant professors of electrical and computer engineering, Devi Parikh and Dhruv Batra’s project on artificial intelligence in collaboration with Microsoft, Visual Question Answering (VQA), was featured in Bloomberg Business. Visual Question Answering is a new dataset containing open-ended questions about images. The system takes an image as an input and a question about that image, then produces an answer as an output.  To read more of the article click here.


Samah Gad, DAC (CS) PhD graduate, and Hussein Ahmed launch a successful startup

Samah_Hussein

Hussein Ahmed (left), middle? , Samah Gad (right)

Transpose, a new Seattle startup that bills itself as a holistic information management platform, today announced a $1.5 million funding round. Transpose is the brainchild of Samah Gad, DAC (CS) PhD graduate and Hussein Ahmed also a CS PhD graduate. Formerly known as KustomNote, the nine-person company has developed software that helps customers create structure and pull intelligence from large sets of data across all devices.

Seattle-based venture capital firm Founder’s Co-op led the round, which also included participation from Alliance of Angels and New York-based The Gramercy Fund.

The startup, which graduated from Seattle-based B2B accelerator 9MileLabs this past November, originally built structured note-taking templates that helped customers record, store, retrieve, and share custom-structured notes.

Now, Transpose has evolved to also pull insights from unstructured data, files, and voice recordings by using cloud-based data retrieval technologies and text analytics.

Tranpose CEO Hussein Ahmed said there are more than 90,000 users on the platform, including employees from companies like Apple, Walmart, and Heineken. Clients use the system to do everything from storing and tracking wine collections, to organizing schedules and vaccinations for children.

“It’s a complete do-it-yourself solution for consumers and teams in enterprises to build their very own solution to track assets, manage leases, or sales leads,” Ahmed explained. Read more at


Devi Parikh and Dhruv Batra receive COE Outstanding New Assistant Professor Awards

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DAC faculty members Devi Parikh and Dhruv Batra, assistant professors of electrical engineering received Outstanding New Assistant Professor Awards.  They were presented with the awards at the eighteenth annual Virginia Tech College of Engineering faculty reception.  They were awarded for teaching innovation, research, service, and outreach for 2015.  To read more about their awards click here. 


Dhruv Batra’s upcoming CVPR work covered in the Boston Globe

photos-brainiac

In the online, big data world, it’s important to be able to separate the wheat from the chaff. This is true when it comes to refining search results and culling a Twitter feed, and it’s true with photographs, too. Dhruv Batra’s latest innovation recently posted to arXiv.org takes advantage of all sorts of social and technological cues to figure out who really matters in an image. “We have the ability to look at a scene and, just by coding what people are doing, how people are looking at each other, we can get a sense of the important actors,” says Dhruv Batra, a professor of electrical and computer engineering and creator of the program, along with graduate student and lead designer Clint Solomon Mathialagan and Andrew Gallagher, an engineer at Google. Read more. 


Newsweek profiles DAC’s EMBERS project

One of the many protests against the 2014 World Cup in Sao Paulo, May 15, 2014.

One of the many protests against the 2014 World Cup in Sao Paulo, May 15, 2014.

Newsweek profiles the Discovery Analytics Center’s EMBERS Project, which is funded by IARPA.  EMBERS offers a glimpse into just how much “big data” has changed the game by magnifying the U.S. intelligence community’s ability to forecast—with phenomenal accuracy—human behavior on a global scale by scouring Twitter, YouTube, Wikipedia, Tumblr, Tor, Facebook and more. EMBERS is using algorithms and a variety of advanced tools to sort through dense and complex information for patterns in the chaos—patterns that frequently point to events before they happen, such as civil uprisings, disease outbreaks, humanitarian crises, mass migrations, protests, riots, political routs, even violence. Click here to read more.


Big- Data Project on 1918 Russian Flu Highlights DAC Collaboration with Humanities Researchers

Soldiers with the Spanish flu are hospitalized inside the U. of Kentucky gym in 1918. In one prevention method examined in a new study, New Yorkers were advised to refrain from kissing “except through a handkerchief.”

Soldiers with the Spanish flu are hospitalized inside the U. of Kentucky gym in 1918. In one prevention method examined in a new study, New Yorkers were advised to refrain from kissing “except through a handkerchief.”

An article in the Chronicle of Higher Education today highlights possibilities in interdisciplinary research between data analysts and humanities researchers. It showcases DAC’s Digging into Data project as a “model-in-progress for how data-driven analysis and close reading can enhance each other”. The research focuses on several questions: How did reporting on the Spanish flu spread in 1918? And how big a role did one influential person play in shaping how the outbreak was handled? Read More


DAC student Sathappan Muthiah receives Deployed Application Award at IAAI

sathappan-updatedCongratulations to DAC/CS PhD Student Sathappan Muthiah on receiving Deployed Application Award at IAAI (Conference on Innovative Applications of Artificial Intelligence) 2015 for his paper “Planned Protest Modeling in News and Social Media“. The CS department also recognized his work with a Pratt fellowship for Spring 2015 – Congratulations twice!


CT Lu receives grant from the US Army

nvc-11Chang-Tien Lu, associate director of DAC and associate professor of computer science has been awarded a $300,000 subcontract from the United States Army Research Office and United States Army Engineer Research and Development Center.  He will use the grant to develop an automated tool to make sense of data captured in news articles, tweets, images, and audio and video streams.

Naren Ramarkishnan, director of DAC and professor of computer science along with Ing-Ray Chen, also a professor of computer science are co-principle investigators of the grant.  They will help Lu oversee the projects research.  To read more about grant click here.

 


The EMBERS is featured on the cover of the Big Data Journal (Dec 2014 issue)

Venezuelan Spring EMBERS predictions

As featured in the Big Data Journal: “Forecasting has long been a mystic art with techniques shrouded in mystery. Approaches from big data and machine learning are now revolutionizing the science of predictive analytics. The EMBERS system has been producing early warnings of civil unrest across Latin America for over two years. In February 2014, EMBERS forecast the occurrence and spread of student-led protests in Venezuela days in advance. For more information, please see the article by Doyle and colleagues in this issue of Big Data.” Read more


Press Coverage on Devi Parikh’s work in AI

Devi Parikh

Devi Parikh, assistant professor in the department of electrical and computer engineering and DAC faculty member received close to $1 million “to teach machines to use ‘common sense’ in image analysis.” Parikh, who leads the Computer Vision Lab at Virginia Tech, is the recipient of the Allen Distinguished Investigator Award from the Paul G. Allen Family Foundation. She’s using the money to help computers “read” complex images with the use of cartoon clip art scenes. To read more about Devi’s grant click here.

 


Devi Parikh’s award featured in VTNews

Devi Parikh

Devi Parikh, an assistant professor in the Bradley Department of Electrical and Computer Engineering and DAC faculty member at Virginia Tech, has received an Allen Distinguished Investigator Award for close to $1 million from the Paul G. Allen Family Foundation to teach machines to use “common sense” in image analysis. Parikh uses cartoon scenes crafted from clip art to help computers “read” complex images. “Humans interpreting visual scenes can take advantage of basic knowledge about how objects typically interact, but computers,” Parikh said, “don’t have the same skill”.

“The visual world around us is bound by common sense laws depicting birds flying and balls moving once they’ve been kicked, but much of this knowledge is hidden from the eyes of a computer,” she said. Computers, in other words, might have a lot of information about avian wing structure, but they don’t necessarily know that birds fly.

“Simply labeling images with this information does not address the underlying problem of how it all fits together,” said Parikh. “We need a dense sampling of the visual world to understand how subtle changes in the scene can change its overall meaning.”

Parikh proposes to use crowdsourcing, leveraging hundreds of thousands of Amazon Mechanical Turk workers (or “Turkers”) online to illustrate the visual world using clip art.

The Turkers will use clip art to create scenes with visual features and basic written depictions of what’s going on. By learning to associate certain visual elements with the information in the text, the computer may eventually accumulate a lexicon of common sense that will help it understand the visual world like humans do.

“These clip art scenes will serve as a completely new and rich test bed for computer vision researchers interested in solving high-level AI problems,” said Parikh, who will be collaborating with Larry Zitnick and Margaret Mitchell at Microsoft Research. Zitnick is in the Interactive Visual Media group and Mitchell specializes in Natural Language Processing.

“Learning common sense will make our machines more accurate, reasonable and interpretable — all imperative towards integrating artificial intelligence into our lives and society at large,” said Parikh.

So while machines today can play chess, vacuum floors, and win at Jeopardy, Parikh’s research could take them a step closer to being intelligent entities. That’s critical for a variety of artificial intelligence applications — be it for personal assistants, health care, autonomous driving, or security, such as law enforcement or disaster recovery purposes.

The award is part of the Allen Distinguished Investigators Program, which was established to advance ambitious, breakthrough research in key areas of science. Parikh is also a recipient of the Army Research Office Young Investigator Award, and of two Google Faculty Research Awards.

Parikh leads the Computer Vision Lab at Virginia Tech. She is also a member of theDiscovery Analytics Center, which has operations on the Blacksburg campus and also at the Virginia Tech Research Center in Arlington. The center is housed in the Department of Computer Science within the College of Engineering. She is also a member of the Virginia Center for Autonomous Systems at Virginia Tech. Both centers benefit from the support of theInstitute for Critical Technology and Applied Science for their interdisciplinary research.

A premier Research Institute of Virginia Tech, the Institute for Critical Technology and Applied Science ensures a sustainable future by advancing transformative, interdisciplinary research at the intersections of engineering, the humanities, and the physical, life, and social sciences.

Devi Parikh has been named a 2014 Allen Distinguished Investigator

Devi Parikh, Asst Professor, Electrical and Computer Engineering.

Congratulations to Devi Parikh who has been named a 2014 Allen Distinguished Investigator! Devi’s work will impart common sense reasoning to computers to accomplish human-like visual recognition. She is in great company! Read More


Parang Saraf’s VAST grand challenge award is the NCR highlight of the week

Parang Saraf

Parang Saraf, a DAC/CS Ph.D. student in the National Capital Region, recently accepted the VAST Challenge 2014 Grand Challenge Award for Effective Analysis and Presentation in Paris, France. The VAST Challenge provides an opportunity for visual analytics researchers to test their innovative thoughts on approaching problems in a wide range of subject domains against realistic datasets and problem scenarios. The award was presented during the IEEE Vis Conference, where Saraf spoke for 30 minutes about the team’s solution to the challenge.

The VAST Challenge provides an opportunity for visual analytics researchers to test their innovative thoughts on approaching problems in a wide range of subject domains against realistic datasets and problem scenarios.

The award was presented during the IEEE Vis Conference, where Saraf spoke for 30 minutes about the team’s solution to the challenge.

Saraf led the winning Virginia Tech team which also included Patrick Butler, a recent Ph.D. graduate from the Department of Computer Science in Blacksburg who is currently working with the U.S. Army Corps of Engineers.

VAST Challenge 2014 was comprised of three Mini-Challenges and one Grand Challenge. The data sets included unstructured news articles, email headers, GPS data, financial transaction data and real-time streaming data. Only the teams who finished all three mini challenges were allowed to submit to the grand challenge.

In total there were 77 submissions for all the challenges and only seven teams progressed to the Grand Challenge. The Virginia Tech team submitted to all three Mini-Challenges and in addition to the Grand Challenge Award, won an honorable mention for Effective Presentation in Mini-Challenge 2.

Saraf’s research area is data mining with specific interests in social media analytics and data visualization. He works on theOpen Source Indicators (OSI) EMBERS project supervised by Discovery Analytics Center Director Naren Ramakrishnan at the Virginia Tech Research Center — Arlington.


EMBERS Featured in Virginia Tech Magazine

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The EMBERS project, sponsored by IARPA was featured in a major spread of the Virginia Tech Magazine.

Through the use of big data, Naren Ramakrishnan and his team from the computer science department’s Discovery Analytics Center (DAC) may make forecasting the future as commonplace as forecasting the weather.

The term “big data” refers to the use of algorithms and other tools to train computers to spot trends in collections of information that are too massive and complex to analyze with traditional methods. The proliferation of data has accelerated with the integration of computers into our daily lives, from social media on our phones to tracking buying habits at the grocery store.

Virginia Tech’s efforts stand at the forefront of the big data movement, with labs and professors across the commonwealth conducting increasingly data-driven research as the university looks to build additional capacity for future initiatives. Maintaining a strong presence in Blacksburg as well as in the National Capital Region allows for significant collaborations in the domains of intelligence analysis, national security, and health informatics.

“To Virginia Tech’s researchers, big data represents an important opportunity to create knowledge and provide insight by leveraging large, potentially unstructured data sets,” said Scott Midkiff, the university’s vice president for information technology and chief information officer and a professor in the Bradley Department of Electrical and Computer Engineering.

Projects like DAC’s EMBERS and the Virginia Bioinformatics Institute’s (VBI) Network Dynamics and Simulation Science Laboratory (NDSSL), which simulates disasters to evaluate emergency response and disaster preparedness policies, are telling examples of big data’s potential. Read more. 


Analysis by DAC CS PhD candidate Prithwish Chakraborty about the US flu season

prithwish-updatedPrithwish Chakraborty, DAC/CS PhD student is helping organize the Flu Forecasting questions on the SciCast prediction market  (https://scicast.org/flu) this year. Participants are required to predict several flu season characteristics, at national and at regional levels (10 HHS regions). Read his analysis


Briefing to VA Secretary of Technology Karen Jackson and Sen Mark Warner’s staff

Naren Ramakrishnan

Naren Ramakrishnan, director of the Discovery Analytics Center and Bryan Lewis, public health policy analyst, Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, presented research being done in their respective laboratories in a briefing to Virginia Secretary of Technology Karen Jackson. Senator Mark Warner’s staff were also in attendance. It was a great opportunity to brief them and present DAC’s cutting-edge research in forecasting and analytics.

Karen Jackson, Secretary of Technology for the Commonwealth of Virginia, was welcomed to the Virginia Tech Research Center – Arlington, Monday, Oct. 27, for a briefing on national security and data sciences research taking place in the National Capital Region.

“This visit was an excellent opportunity to brief Secretary Jackson on a number of programs in cyber and national security, data analytics, and complex systems modeling and simulation, including capabilities that could help the Commonwealth prepare and respond to future challenges, such as cyber attacks on critical infrastructure and public health emergencies, such as an Ebola outbreak,” said Sanjay Raman, associate vice president for the National Capital Region.

Read More


Visual Analytics Team Awarded $1 Million NSF Grant

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Members of the Visual Analytics team include (from left) Xinran Hu, Chris North, Leanna House, Scotland Leman, Lauren Bradel, Jessica Zeitz Self, and Ian Crandell.

Big Data: Everyone wants to use it; but few can. A team of researchers at Virginia Tech is trying to change that.

In an effort to make Big Data analytics usable and accessible to nonspecialist, professional, and student users, the team is fusing human-computer interaction research with complex statistical methods to create something that is both scalable and interactive.

“Gaining big insight from big data requires big analytics, which poses big usability problems,” said Chris North, a professor of computer science and associate director of the Institute for Critical Technology and Applied Science’s Discovery Analytics Center.

With a $1 million from the National Science Foundation, North and his team are working to make vast amounts of data usable by changing the way people see it.

Yong Cao, an assistant professor with the Department of Computer Science in the College of Engineering, along with Leanna House, an assistant professor, and Scotland Leman, an associate professor, both with the Department of Statistics of the College of Science, are working with North to bring large data clouds down to manageable working sets. Read more.


Devi Parikh’s Project Covered by AAAS

Devi ParikhAn enormous gap exists between human abilities and machine performance when it comes to understanding the visual world from images and videos. Humans are still way out in front.

“People are the best vision systems we have,” said Devi Parikh assistant professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. “If we can figure out a way for people to effectively teach machines, machines will be much more intelligent than they are today.”

In her research, Parikh is proposing to use visual abstractions or cartoons to teach machines. She works from the idea that concepts that are difficult to describe textually may be easier to illustrate. By having thousands of online crowd workers manipulate clipart images to mimic photographs, she seeks to teach a computer to understand the visual world like humans do. Read more. 


EMBERS featured in the Wall Street Journal

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Analysts for the Central Intelligence Agency, the National Security Agency and more than a dozen other government organizations depend on their ability to forecast national and global events to help ward off various threats to the country, but old-style approaches can produce flawed results. Read more


Dhruv Batra’s Project Featured in VT News

dhruv_batra_200When Dhruv Batra of the Virginia Tech College of Engineering travels in September to Zurich for the 2014 European Conference on Computer Vision, he will be a rising star in the growing field of vision and pattern recognition in computers.

The assistant professor with Virginia Tech’s Bradley Department of Electrical and Computer Engineering previously co-led a tutorial in the research field at another industry conference in Ohio this past June. On his way to Zurich, Batra will give talks on the same subject — creating software programs that help computers “see” and understand photographs just as humans can – at software giant Microsoft’s research lab at Cambridge University and then a separate event at Oxford University, both in the United Kingdom.

The travel comes on the heels of Batra’s spring acceptance of three major federal research grants worth than more a combined $1 million: A National Science Foundation CAREER Award, a U.S. Army Research Office Young Investigators Award, and an U.S. Office of Naval Research grant.

The awards — valued at $500,000 for five years for the CAREER Award, $150,000 for three years from the Army, and $360,000 for three years from the Navy, all focus on machine learning and computer vision — creating algorithms and techniques that will teach computers to better understand photographic images, and quickly so. Read more.


Lenwood Heath Oversees Implementation of Revolutionary Naming System for Organisms

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Lenwood Heath, DAC faculty member, is working with Boris Vinatzer, associate professor in the College of Agricultural and Life Sciences who has developed a new way to classify and name organisms based on their genome sequence and in doing so created a universal language that scientists can use to communicate with unprecedented specificity about all life on Earth.  Heath oversaw the development of the bioinformatic pipeline to implement the system. He was interested in collaborating with Vinatzer because of the potential to empower scientists to communicate accurately with one another about biological systems. To read more about their collaboration click here.


CloudCV continues to make a splash!

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Congrats to Dhruv Batra for his Windows Azure for Research Award! Microsoft will provide one year of computing and storage support to CloudCV on their Azure cloud platform.

Microsoft Research’s Windows Azure for Research program, which features a continuing series of Windows Azure cloud training events and a program of Windows Azure research grants, has been going strong since its launch in September 2013. As the December 15, 2013, deadline for the second round of grant proposals approached, we braced ourselves for a barrage of creative ideas. We weren’t disappointed, receiving proposals from every continent (well, except Antarctica). The response was particularly strong from such countries as Brazil and China, where our recent training events gave researchers an excellent, hands-on view of the capabilities of Windows Azure.

For more visit here


Samah Gad’s Research Covered by the American Historical Association

IMG_ewing-figure1(635x400)The new methods of “big data” analysis can inform and expand historical analysis in ways that allow historians to redefine expectations regarding the nature of evidence, the stages of analysis, and the claims of interpretation.1 For historians accustomed to interpreting the multiple causes of events within a narrative context, exploring the complicated meaning of polyvalent texts, and assessing the extent to which selected evidence is representative of broader trends, the shift toward data mining (specifically text mining) requires a willingness to think in terms of correlations between actions, accept the “messiness” of large amounts of data, and recognize the value of identifying broad patterns in the flow of information.2

Our project, An Epidemiology of Information, examines the transmission of disease-­related information about the “Spanish flu,” using digitized newspaper collections available to the public from the Chronicling America collection hosted by the Library of Congress. We rely primarily on two text mining methods: (1) segmentation via topic modeling and (2) tone classification. Although most historical accounts of the Spanish flu make extensive use of newspapers, our project is the first to ask how looking at these texts as a large data source can contribute to historical understanding of this event while also providing humanities scholars, information scientists, and epidemiologists with new tools and insights. Our findings indicate that topic modeling is most useful for identifying broad patterns in the reporting on disease, while tone classification can identify the meanings available from these reports. Read more.


Congrats to C.T-Lu and his students

embers copyCongrats to C.T-Lu and his students whose paper on finding the breadcrumbs of civil unrest on Twitter has been picked as a Jan 2014 highlight by the IEEE Special Technical Community (STC) on Social Networking! For more details visit here


Congratulations to DAC PhD (CS) graduate Feng Chen

22eb386Congratulations to DAC PhD (CS) graduate Feng Chen (advisor: CT Lu) who has accepted a faculty position at SUNY, Albany! Feng joins in Jan 2014.


Congrats to Dhruv Batra on Amazon Web Services in Education Grant

dhruv_batra_200Congrats to Dhruv Batra who has received an Amazon Web Services in Education grant for developing CloudCV, a cloud-based computer vision platform for processing big visual data. CloudCV provides APIs for MATLAB and Python as well as a web front-end, and will benefit both experts and non-experts who desire to analyze image data. For more go to CloudCv


Congratulations to Aditya Prakash on NSF Grant

badityap-portraitCongratulations to DAC faculty member Aditya Prakash for his new NSF award entitled: “Immunization in Influence and Virus Propagation on Large Networks”! Aditya is exploring the question: given a graph, like a social network or the blogosphere, in which an virus (or meme or rumor) has been spreading for some time, how to select the k best nodes for immunization/quarantining immediately? The work has several applications in public health and epidemiology, viral marketing and social media like Twitter.


Congratulations to DAC PhD alumnus Alex Endert

COC Faculty/Staff portraits at Klaus.

COC Faculty/Staff portraits at Klaus.

Close on the heels of DAC PhD alumnus Alex Endert winning the outstanding dissertation award in the CS department, he is designated the recipient of the first ever annual IEEE VGTC Best Doctoral Dissertation Award! Congrats Alex and advisor Chris! The award was presented at the IEEE VIS Conference in October 2013.


DAC PhD student Ji Wang and DAC alumnus Sheng Guo are Round One Winners of the Yelp Dataset Challenge

Yelp_Logo_No_Outline_ColorCongratulations to DAC PhD student Ji Wang (advisor: Chris North), and DAC alumnus Sheng Guo who, along with U. Toronto grad student Jian Zhao, Round One Winners of the Yelp Dataset Challenge! They are in good company: other winners are from CMU, Stanford, and Berkeley.


DAC student Huijuan Shao wins Best Student Paper Award in the Computational Sustainability Track at AAAI’13

huijuan-updatedCongratulations to DAC PhD student Huijuan Shao for her Best Student Paper Award in the Computational Sustainability Track at AAAI’13! She receives $750 from CRA/CCC.


Dhruv Batra received a Google Research Award for his work in natural language processing

dhruv_batra_200Dhruv Batra’s research, with Chris Dyer, Kevin Gimpel and Greg Shakhnarovich, won a Google Research Award. Congratulations Dhruv!