News featuring Rachel Szabo

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.





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

Screen Shot 2017-02-08 at 11.35.25 AM

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


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


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


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


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

Map showing geographical distribution of job postings in Virginia, featured on

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

chris north

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


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


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


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


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


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


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


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


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


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


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 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


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  ( 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


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


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


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!


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!