Snehasis Mukhopadhyay
Professor of Computer Science, Indianapolis
Joined department: Fall 2024
Dr. Snehasis Mukhopadhyay is a Professor of Computer Science at Purdue University, Indianapolis. He received his Ph.D. from Yale University, and a Master’s degree from the Indian Institute of Science, Bangalore, India. He received an NSF CAREER award for integrating research and education. He has continuously received research funding from government agencies including NSF, NIH, NOAA, and USDA. He has twice received the Indiana University Trustees’ Teaching Awards (TTA) in the years 2014 and 2017. He has provided extensive professional services including being NSF Review panelist on many occasions, and being the General Chair of the 2016 ACM International Conference on Information and Knowledge management (CIKM). In the year 2023, he served as an invited member of the National Academies Panel on Assessment of Military Information Sciences Research Program at the Army Research Laboratory, Aberdeen, Maryland. His current research interests are in the areas of Artificial Intelligence, Machine Learning, Interactive Data Science, and AI in Medicine. He has more than 100 peer-reviewed research articles in these areas.
Selected Publications
Li, H., Fang, S., Mukhopadhyay, S., Saykin, A.J. and Shen, L., 2018, December. Interactive machine learning by visualization: A small data solution. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 3513-3521). IEEE.
Mukhopadhyay, S., Tilak, O. and Chakrabarti, S., 2018, December. Reinforcement learning algorithms for uncertain, dynamic, zero-sum games. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 48-54). IEEE.
Babbar-Sebens, M., Mukhopadhyay, S., Singh, V.B. and Piemonti, A.D., 2015. A web-based software tool for participatory optimization of conservation practices in watersheds. Environmental Modelling & Software, 69, pp.111-127.
Bhuiyan, M., Mukhopadhyay, S. and Hasan, M.A., 2012, October. Interactive pattern mining on hidden data: a sampling-based solution. In Proceedings of the 21st ACM international conference on Information and knowledge management (pp. 95-104).
Mostafa, J., Mukhopadhyay, S., Palakal, M. and Lam, W., 1997. A multilevel approach to intelligent information filtering: model, system, and evaluation. ACM Transactions on Information Systems (TOIS), 15(4), pp.368-399.