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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.

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Wei Wang graduated with a Ph.D. in computer science in 2017 and joined the Language and Information Technology (LIT) group at Microsoft Research, Redmond, Washington, as an applied scientist. Recently, he was promoted to senior applied scientist.

Did transitioning from academia to industry hold any real surprises for you?

For the most part, problems that we try to solve as Ph.D. students are well-defined and have benchmarks. We just need to propose novel approaches to push the-state-of-the-art. The problems I face now often require much more effort to build an end-to-end solution.

What are your responsibilities at Microsoft Research?

I mainly work in the area of natural language understanding and user behavior modeling. I also collaborate with the product team to transfer state-of-the-art technique to the product.

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Sirui Yao studies the biases of recommender systems.

“A recommender will often suggest different courses to male and female college students because based on historical data, there are differences in course preference between these two groups,” said Yao, a Ph.D. student in computer science at the Discovery Analytics Center.

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Interested in data mining and machine learning, Taoran Ji, a Ph.D. student in computer science, said he was drawn to the Discovery Analytics Center because it plays an active role in these fields.

“There are so many projects at the center that provide great opportunities to practice these techniques in real world applications,” Ji said.

Ji, advised by Chang-Tien Lu, has focused his research on a range of topics, all of which he has been able to explore by collaborating with Lu and other faculty and students at DAC. These include event detection/prediction and associated applications such as civil unrest detection, airport threat detection, transit disruption detection, and emerging science and technology prediction.

Among his published papers, two were included in proceedings at conferences held this year.

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Reports that medical errors are the third leading cause of death in the United States have led the Institute of Medicine and several state legislatures to suggest that data from patient safety event reporting systems could help health care providers better understand safety hazards and, ultimately, improve patient care.

“Tens of thousands of these safety report databases provide a free text field that does not constrain the reporter to fixed, predefined categories,” said Srijan Sengupta, assistant professor of statistics in the College of Science and a faculty member at the Discovery Analytics Center.

Sengupta has received an $815,218 Research Project Grant (R01) from the National Institutes of Health to develop novel statistical methods to analyze such unstructured data in safety reports. Click here to read more about Senputa’s grant.

In disease control and prevention, understanding how an emerging infectious disease can spread beyond the visible network is important.

Marketers posting ads on an online social network can benefit from knowing how their information will spread beyond ego networks.

These two scenarios provide good examples for practical application of Shuangfei Fan’s research using deep representation learning algorithms on labeled graphs to model graph generation and graph evolution.

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Alexander Rodriguez was a master’s degree student in data science at the University of Oklahoma when he met B. Aditya Prakash at the 2017 ACM SIGKDD Conference on Knowledge Discovery and Data Mining. That meeting sealed his decision to apply to the Ph.D. computer science program at Virginia Tech.

“I was excited about the problems he was working on and I felt he was a knowledgeable person from whom I could learn how to become a researcher,” said Rodriguez of his current advisor.

The opportunity to be part of the Discovery Analytics Center also played a part in his decision to come to Virginia Tech.

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Mahmud Shahriar Hossain was recently promoted to associate professor of computer science, with tenure, at the University of Texas at El Paso (UTEP). He leads the university’s Discovery Analytics Lab. Hossain earned his Ph.D. in computer science from Virginia Tech in 2012 and joined UTEP as an assistant professor in 2013.

While at the Discovery Analytics Center at Virginia Tech, his work with advisor, Naren  Ramakrishnan focused on event analysis, “storytelling,” and data abstraction techniques like alternative clustering and scatter/gather clustering. He applied his methods to solve a broad spectrum of problems in multiple disciplines, including national security, biomedical science, and mechanical engineering.

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