معرفی
Nishant Mehta is an Associate Professor in the Department of Computer Science at the University of Victoria, Canada. He serves as the Combined Program Advisor for Statistics/CSC. His research focuses on machine learning theory, including online learning, statistical learning theory, and reinforcement learning. He has held postdoctoral positions at CWI (Amsterdam) and ANU (Canberra), and earned his PhD from Georgia Tech (2013).
Education: PhD in Computer Science (2013) and BS in Computer Science (2005), both from Georgia Institute of Technology.
Research interests emphasize theoretically grounded methods in machine learning, including multi-armed bandits, PAC-Bayesian methods, and learning with self-interested agents. He has supervised multiple PhD and MSc students, including current advisees Quan Nguyen and Ali Mortazavi.
Awards include the 2025 Best Reviewer Award for AISTATS and 2016 Outstanding Reviewer Award for NIPS. His work spans over 50 publications in top venues like NeurIPS, ICML, and COLT. He has held grants from NSERC and JPMorgan Chase, focusing on topics like representation learning and privacy-aware online learning.
Teaching includes Machine Learning Theory, Data Mining, and Algorithms courses. He has been an Action Editor for TMLR and served as Area Chair for multiple conferences including NeurIPS and ICML.
