The Sanghani Center is home to high-profile research, garnering recognition within and beyond the data analytics community.
Our talented team has been recognized with many competitive research awards and featured in major news and media outlets such as the Wall Street Journal, Newsweek, the Boston Globe and the Chronicle of Higher Education.
Andreea Sistrunk, DAC Ph.D. student in Department of Computer Science
Graphic is from the paper “REGAL: A regionalization framework for school boundaries”
When Andreea Sistrunk started taking classes at Virginia Tech in the fall of 2014 she had left her job as a full time teacher in northern Virginia to devote more time to her two young daughters, ages three and seven.
“It was becoming more difficult for me to hold a full time job and be a good mother so I chose to take a break from work,” Sistrunk said. “I used a sort of ‘mom’s night out’ to enroll in a graduate course at Virginia Tech because I really missed learning new things.”
Nikhil Muralidhar, DAC and UrbComp Ph.D. student in the Department of Computer Science
Graphic is from Muralidhar’s paper on “PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly”
Choosing to pursue a Ph.D. in computer science at Virginia Tech was easy for Nikhil Muralidhar.
“Virginia Tech was my top choice for good reason,” Muralidhar said. “It is known for its quality research and interdepartmental collaborations, for encouraging students to work on real world interdisciplinary applications, and for pioneering programs like UrbComp.”
“I had been following DAC’s track record of high quality, practical research since I was a Virginia Tech undergraduate. I am happy to be part of a rare breed of research labs with both extensive industrial and academic collaborations. The facilities are state-of-the-art and the faculty are approachable, helpful, and use their experience to guide their students to become successful researchers,” said Muralidhar, who is advised by Naren Ramakrishnan.
Lei Zhang, DAC Ph.D. student in the Department of Computer Science
Graphic is from Zhang’s research on “Situation-Based Interpretable Learning for Personality Prediction in Social Media”
Lei Zhang was a master’s degree student in software engineering at Jinan University in China when his advisor told him about meeting Chang-Tien Lu from Virginia Tech and how he was doing research with algorithms on Twitter. While they were using different platforms — Zhang’s own work was on Weibo, the largest Chinese microblogging website — he was interested to hear about Lu’s research.
When he decided to pursue a Ph.D., Zhang decided to apply to Virginia Tech’s Department of Computer Science. As it turned out, Lu is now his advisor.
“A rumor generally refers to an interesting piece of information — widely disseminated through a social network — that is not easy to substantiate,” said Islam, a Ph.D. student in computer science.
Later, it can turn out to be true, false, or remain unverified.
“The threat of rumors and fake news is very real and identification is crucial because rumors and fake news can lead to deleterious effects on users and society,” he said. “For example, spreading unverified malicious content could cause severe economic downfalls within a short period of time.”
The objective of his research, he said, is to develop a range of machine learning methods to effectively detect and characterize rumor veracity in social media.
The research involves analyzing, record-by-record, thousands of lines of export and import data.
“By analyzing available timber data, along with open source domain knowledge, we are trying to develop software and algorithms that will help flag suspicious timber at the border in real time. Such a human-machine approach can improve both efficiency and effectiveness,” Datta said.
Having already earned a bachelor of arts degree in English language from Umm Al-Qura University and a master of arts in English literature from King AbdulAziz University, Alhamadani made a decision to combine his knowledge of linguistics with computer science. That resolve led him to the University of New Hampshire, where he earned a master of science degree in computer science.
Now, as a Ph.D. student in computer science at the Discovery Analytics Center, Alhamadani is focusing on Arabic natural language processing, especially text summarization and text classification. Advised by Chang-Tien Lu, his work involves automatic archiving of news without human annotation and summarizing daily news articles to headlines.
Graphic is from Choi’s paper on “Why Can’t I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition”
Jinwoo Choi, DAC Ph.D. student in the department of Electrical and Computer Engineering
Jinwoo Choi will be heading to Snowmass Village, Colorado, in March to present “Unsupervised and Semi-Supervised Domain Adaptation for Action Recognition from Drones” during the 2020 Winter Conference on Applications of Computer Vision. WACV is a premier meeting of the IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence.
“We are very proud of our graduates and the impactful research they have undertaken at DAC while pursuing their graduate degrees,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the center. “We wish them continued success as they embark on their academic and industry careers.”
Following are the DAC graduates:
Shuangfei Fan, advised by Bert Huang, received a Ph.D. in computer science. Her research interests are machine learning, graph analysis and deep learning, and her dissertation title is “Deep Representation Learning on Labeled Graphs.” Fan joins Facebook as a research scientist. In that position she will work to apply machine learning techniques to help people build community and bring the world closer together.
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.