Ambuj Tewari

Ambuj Tewari is a Professor in the Department of Statistics and holds a courtesy appointment in the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor. His research focuses on theoretical foundations of machine learning, reinforcement learning, and optimization, with applications in chemistry, psychiatry, and healthcare. He has received prestigious awards including the IISA Early Career Award (2023), Sloan Research Fellowship (2017), and NSF CAREER Award (2015).

Education: PhD in Computer Science (2007) from UC Berkeley, M.A. in Statistics (2005), and B.Tech. in Computer Science from IIT Kanpur (2002). Prior roles include postdoctoral fellowships at UT Austin and research positions at TTIC.

Research interests emphasize rigorous analysis of AI/ML models, including reinforcement learning algorithms, high-dimensional statistics, and applications in chemistry (e.g., reaction prediction) and mental health (precision medicine interventions). Notable contributions include work on contextual bandits, conformal prediction, and federated learning in healthcare.

His 150+ publications span top venues like NeurIPS, ICML, COLT, and journals such as Journal of Machine Learning Research. Current projects include AI-driven drug discovery, privacy-aware mHealth systems, and operator learning for PDEs.

Key awards: Outstanding Paper Award (ALT 2025), First Place in DREAM Challenge (2024), IMS Fellowship (2022), Adobe/Facebook Research Awards (2020–2021).

Teaching and service include leadership in the Michigan Institute for Data Science (MIDAS) and editorial roles at Journal of Machine Learning Research. Active in promoting AI ethics and privacy-by-design principles in healthcare technologies.