This seminar will consist of short introductions to the research of three new faculty members in the Department of Computer Science.
Jiepu Jiang holds the Ph.D. in Library & Information Science from the University of Pittsburgh. He is completing a second Ph.D., in Computer Science, at the University of Massachusetts Amherst. His research aims to help people better access and use information, including both designing effective systems to support information access and studying human factors during this process. He has been regularly publishing and serving in top information retrieval and data mining conferences. He received the best student paper award from CHIIR 2017 for his work on understanding dynamics of search result judgments in information retrieval.
Anuj Karpatne develops data mining methods for solving scientific and socially relevant problems. He has published more than 25 peer-reviewed articles at top-tier conferences and journals (e.g., KDD, ICDM, SDM, TKDE, and ACM Computing Surveys), given multiple invited talks, and served on panels at leading venues (e.g., SDM and SSDBM). His research has resulted in a system to monitor the dynamics of surface water bodies on a global scale, which was featured in an NSF news story. He is also a co-author of the second edition of the textbook, “Introduction to Data Mining.” Anuj received his Ph.D. in 2017 from the University of Minnesota under the guidance of Prof. Vipan Kumar. Before joining the University of Minnesota, Anuj received his bachelor’s and master’s degrees from the Indian Institute of Technology Delhi.
Bimal Viswanath joined the faculty at Virginia Tech after a stint as a Postdoctoral Scholar at the University of California Santa Barbara. Prior to that, he was a Researcher at Nokia Bell Labs, Germany for a year. He received his PhD (2016) and M.S (2008) from the Max Planck Institute for Software Systems, Germany and the Indian Institute of Technology Madras, India, respectively. He is primary research interests are in security and privacy, and his recent work explores the risks posed by deep learning in different application scenarios.