News

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

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

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

Ph.D.

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

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

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

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

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

Master’s Degree

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

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

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

DeepOutbreak, a team of researchers from Virginia Tech, Georgia Tech, and the University of Iowa, has taken first place in the COVID-19 Symptom Data Challenge.

The competition explores how Facebook symptom survey data can enable earlier detection and improved situational awareness of COVID-19 and flu outbreaks that can help both public health authorities and the general public make better decisions.

The first place award, announced by Catalyst @Health 2.0 in late December, and the team’s work will be featured on the Facebook Data for Good blog. Facebook was one of the sponsors of the challenge. Click here to read more about the challenge.

Anika Tabassum, DAC and UrbComp student in the Department of Computer Science

Graphic is from Tabassum’s paper on “Urban-Net: A System to Understand and Analyze Critical Emergency Management”

Urban computing plays a large part in Anika Tabassum’s research at the Discovery Analytics Center as she attempts to answer questions related to critical infrastructure systems: Which power grids/substations are most vulnerable and need immediate action to recover during a hurricane? Which regions are highly affected during a power outage? Are there patterns or similarities in power outages among the connected components?

Tabassum uses optimization and learning-based algorithms when trying to solve energy challenges like these. A Ph.D. student in computer science, she is also a research trainee in the National Science Foundation-sponsored UrbComp graduate certificate program, which is administered through DAC.

Critical infrastructure systems such as power, transportation, communication, and healthcare are crucial for sustaining day-to-day commodity flows vital to national security, economic stability, and public safety, said Tabassum. Failure of even a small part of such systems — caused by any natural or human-made disaster — can trigger widespread cascading failures impacting many other interdependent modules and disrupt the functionality of the entire system.

“It is challenging to understand and analyze large scale data gathered from these systems in terms of graph networks and time-series sensor technologies since they are unstructured and highly dynamic,” said Tabassum. “But extracting information like anomalies, similar patterns, and actionable insights from critical infrastructure systems can help domain experts assess, in a comprehensive manner, the complex interdependencies and failure dynamics over these systems and can also facilitate faster and less expensive decision-making,” said Tabassum.

Last summer Tabassum was a research intern at the Oak Ridge National Laboratory in Oak Ridge, Tennessee, where she applied her data mining and visualization skills in a U.S. Department of Energy (DOE) project on Smart Neighborhood. This study was accepted at the ACM International Workshop On Urban Building Energy Sensing in New York last month.

Previously, she collaborated with the Oak Ridge National Laboratory, DAC alumnus Liangzhe Chen, and her advisor B. Aditya Prakash on ‘“Urban-Net: A System to Understand and Analyze Critical emergency management.” Tabassum presented this paper in the Project Showcase at ACM SIGKDD’19, in Anchorage, Alaska, in August.

Another of their papers, “Data Mining Critical Infrastucture Systems: Models and Tools,” was published in the December 2018 issue of the IEEE Intelligent Informatics Bulletin.

With a bachelor’s degree in computer science and engineering from the Bangladesh University of Engineering and Technology, Tabassum was attracted to DAC because of the potential and interesting research in data mining and applied machine learning it offered.

“I found my advisor’s work exceptionally intriguing and very much suited to my research interest,” she said. “And once I joined DAC I found a strong collaboration of research and extremely friendly and cooperative graduate students.”

When Prakash relocated to DAC’s Arlington location in the fall, Tabassum also moved from Blacksburg.

She is on track to graduate in December 2021 and is aiming for a position as an industry researcher or an academic post-doctoral researcher.

Aditya Prakash (left) and his Ph.D. student Anika Tabassum (right) at DAC in Arlington.

Aditya Prakash, associate professor of computer science and faculty at the Discovery Analytics Center has moved from Blacksburg to DAC’s location at the Virginia Tech Research Center – Arlington.

“My work is frequently motivated by public health, urban computing, and web-related problems and this location is fertile ground for collaborations in these domains,” Prakash said. “Moving to the greater Washington D.C. metro area will help me further expand my research activities, due to its ‘one of its kind’ proximity to government agencies, companies, and hospitals/medical centers.”

In 2018, Prakash received a Faculty Early Career Development (CAREER) Award from the National Science Foundation to help improve national security and public health. His work has been also been funded through grants and gifts from the Department of Energy, the National Security Agency, the National Endowment for Humanities, and from companies like Facebook.

Prakash said that Virginia Tech’s planned Innovation Campus in Arlington and Alexandria would provide greater opportunity not only for research but also for recruiting talented students.

Prakash is currently advising five Ph.D. students in core computer science and other interdisciplinary programs.

Anika Tabassum, who is also a National Science Foundation research trainee in the UrbComp graduate certificate program, has joined Prakash at DAC in Arlington. Alex Rodriguez plans to make the move from Blacksburg in the Spring. They are working on urban computing and sequence mining and epidemiology and graph mining, respectively.

In addition to his research, Prakash is teaching a graduate level course, CS5834:Introduction to Urban Computing, this semester. The course, offered remotely to Blacksburg students as well, is a core course in the UrbComp certificate program administered through the Discovery Analytics Center and covers the fundamentals of the growing area of using analytics to tackle challenges  posed by increasing urbanization.

DAC Ph.D. students Ping Wang (left) and Tian Shi are in Richland, Washington, this summer, where they are interns at the Pacific Northwest National Laboratory.

A number of graduate students at the Discovery Analytics Center have opted for internships and jobs at companies and national laboratories across the country this summer as a way of both benefiting their own research and gaining real world experience.

Following is a list of where they are for the next few months:

Aman Ahuja, a Ph.D. student in computer science, is an applied scientist intern at Amazon in Palo Alto, California. He is on the Amazon Search Team, researching product search techniques. His advisor is Chandan Reddy.

Tyler Chang, a Ph.D. student in computer science, has begun a six-month appointment at Argonne National Laboratory in Chicago, Illinois. He is one of 70 graduate students who received an appointment from the U.S. Department of Energy (DOE) Office of Science Graduate Student Research (SCGSR) to work on his thesis. The goal is to produce a portable multi-objective optimization software which Argonne could utilize in the future. Chang’s advisor is Layne Watson.

Jinwoo Choi, a Ph.D. student in electrical and computer engineering, is a research intern at NEC Labs America, San Jose, California, working on domain adaptation for video. Choi’s advisor is  Jia-Bin Huang.

Chen Gao, a Ph.D. student in electrical and computer engineering, is a research intern on a video completion project at Facebook in Seattle, Washington. He is working on an algorithm that synthesizes missing regions of videos. His advisor is Jia-Bin Huang.

Liuqing Li, a Ph.D. student in computer science, is on the Content Science team at Yahoo! Research in Sunnyvale, California, working on document recommendation through reinforcement learning. His advisor is Edward Fox.

Sneha Mehta, a Ph.D. student in computer science, is a data science intern at the Netflix headquarters in Los Gatos, California. She is researching novel methods to improve machine translation for subtitles. Her advisor is Naren Ramakrishnan.

Shruti Phadke, a Ph.D. student in computer science, is an intern at the Oak Ridge National Laboratory in Oak Ridge, Tennessee. She is working on developing scalable machine learning and Natural Language Processing (NLP) algorithms to detect public sentiment in news and social media. Her advisor is Tanushree Mitra.

Esther Robb, a master’s degree student in electrical and computer engineering, is a research intern at Google in Mountainview, California, where she is working on facial recognition. Her advisor is Jia-Bin Huang.

Alexander Rodriguez, a Ph.D. student in computer science, is a research intern at WalmartLabs in Sunnyvale, California. His advisor is B. Aditya Prakash.

Dhruv Sharma, a master’s student in computer science, is working at Kitware, Inc., in Carborro, North Carolina. As a research and development intern, Sharma’s work includes some medical image processing/machine learning tasks; mining EHR data for prediction of risk, procedure outcome, or other events; and analyzing training needs of healthcare providers. He is advised by Chandan Reddy.

Tian Shi, a Ph.D. student in computer science, is an intern at Pacific Northwest National Laboratory in Richland, Washington, where he is working on machine comprehension and question-answering on clinical notes in the healthcare domain. His advisor is Chandan Reddy.

Shih-Yang Su, a Ph.D. student in electrical and computer engineering, is a research intern at Borealis AI in Vancouver, Canada, working on graph convolution for structural prediction. His advisor is Jia-Bin Huang.

Deepika Rama Subramanian, a master’s student in computer science, is a mobility intern at Lam Research, Fremont, California, working on designing and developing an end-to-end mobile application for field engineers at Lam Research. Her advisor is Tanushree Mitra.

Anika Tabassum, a Ph.D. student in computer science, is a research intern at the Oak Ridge National Laboratory in Oak Ridge, Tennessee, where she will be applying data mining and visualization skills in two U.S. Department of Energy (DOE) projects: “Reynolds Landing Research” and “North American Energy Resilience Model.” Her advisor is B. Aditya Prakash.

Sai Sindhura Tipirneni, a master’s student in computer science, is working in the Quantum Computing Lab at Oak Ridge National Laboratory in Oak Ridge, Tennessee. Her advisor is Chandan Reddy.

Ping Wang, a Ph.D. student in computer science, is an intern at Pacific Northwest National Laboratory in Richland, Washington, where she is working on question answering on electronic medical records using Natural Language Processing (NLP) techniques. Wang’s advisor is Chandan Reddy.

Sirui Yao, a Ph.D. student in computer science, is a research intern at Google AI in New York City, where she is studying noise and bias in dynamic recommender systems. Her advisor is Bert Huang.

Ming Zhu, a Ph.D. student in computer science, is at Amazon in Seattle, Washington. She is an applied scientist intern for Amazon Comprehend Medical, working on Natural Language Processing on medical corpora using deep learning. Zhu’s advisor is Chandan Reddy.

Yuliang Zou, a Ph.D. student in electrical and computer engineering, is a research intern at NEC Labs America in San Jose, California, where he is working on unsupervised scene structure learning. His advisor is Jia-Bin Huang.