News

Virginia Tech’s 2022 Fall Commencement ceremony takes place today.

“We wish our graduates at the Sanghani Center all the best as they receive their Ph.D. and master’s degrees and take the next step toward achieving their career goals,” 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 are the Sanghani Center’s 2022 summer and fall graduates:

Ph.D.

Nikhil Muralidhar, advised by Naren Ramakrishnan and Anuj Karpatne, has earned a Ph.D. in computer science. His research interest is in leveraging machine learning to address problems in scientific applications leveraging data and scientific theory. He also received a graduate certificate in Urban Computing. The title of his dissertation is “Science Guided Machine Learning: Incorporating Scientific Domain Knowledge for Learning Under Data Paucity and Noisy Contexts.”  In Fall 2022, Muralidhar joined the Computer Science Department at Stevens Institute of Technology in Hoboken, New Jersey, as an assistant professor and leads the Scientific Artificial Intelligence (ScAI) Lab to develop scientific machine learning solutions incorporating data and domain knowledge in physics, fluid dynamics, cyber-physical systems and disease modeling.

Xinyue Wang, advised by Edward Fox, has earned a Ph.D. in computer science. His research focuses on web archive processing and analysis infrastructure through distributed computation. The title of his dissertation is “Large Web Archive Collection Infrastructure and Services.” Wang is joining Yahoo in San Jose, California, as a research scientist.

Master’s degree

Huiman Han, advised by Chris North, has earned a master’s degree in computer science. Her research focuses on visual analytics, interactive machine learning, and explainable artificial intelligence. The title of her thesis is “Explainable Interactive Projections for Image Data.” Huimin is joining LinkedIn in Mountain View, California, as a software engineer in Machine Learning.

Sarah Maxseiner, advised by Lynn Abbott, received a master’s degree in electrical and computer engineering. Her thesis is on assessing the quality level of hand-drawn sketches.  

Xinyue Wang was an undergraduate research assistant involved in artificial intelligence and digital library research at the University of North Texas when he had occasion to connect with Edward Fox, professor in Virginia Tech’s Department of Computer Science and faculty at the Sanghani Center and Zhiwu Xie, a professor at University Libraries.

The two are now Wang’s co-advisors as he pursues a Ph.D. in computer science at Virginia Tech. “They are wonderful people and I am grateful to be able to learn from them and work with them,” he said.

Wang’s research interest is digital infrastructure and analytics of the digital library field. His current work involves digital infrastructure design for easy access and analysis of large web archive collections.

“Large web archive collections are rich datasets that are under researched due to their large size and complexity and have become a technical wall for researchers with or without computer science background. Lack of infrastructure design also makes it difficult for smaller institutions to provide easy access on such data,” Wang said. “My research aims to find a solution whereby large web archive collections can be efficiently accessed and analyzed for academia.”

This research, he said, would contribute to building a foundation for many other researchers who are interested in exploring web archive data in various fields.

“At Virginia Tech and the Sanghani Center I have had the opportunity to work with researchers in different fields, trying to use my own computer science expertise to help solve their problems,” Wang said. “I enjoy being in touch with a diverse group of researchers and confronting different real-world problems.”

Wang’s paper, “The Case For Alternative Web Archival Formats To Expedite The Data-To-Insight Cycle,” was included in the proceedings of the 2020 ACM/IEEE Joint Conference on Digital Libraries (JCDL) in 2020.

In previous years, Wang had two posters in JCDL conference proceedings, “Web Archive Analysis Using Hive and SparkSQL” in 2019; and “Towards A Self-Learning Library For Vibration Data” in 2018.

His work on “Metadata records machine translation combining multi‐engine outputs with limited parallel data,” was published in the Journal of the Association for Information Science and Technology In January 2018.

Wang, who earned his bachelor of science degree from the University of North Texas, is projected to graduate in June 2022 and plans to pursue a career in academia.