News

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.

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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.

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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.

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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.

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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 24^th 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.

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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.

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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.

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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.

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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.

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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.

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