I am a PhD student at the Autonomous Learning Lab at the University of Massachusetts, Amherst. I am advised by Bruno Castro da Silva and Philip Thomas. I am interested in applying reinforcement learning in practical applications, and my research focuses on addressing the challenges associated with this. To that end, I focus on problems like robust policy evaluation and model learning under the omnipresent setting of partial observability.
Previously, I was a master’s student at Carnegie Mellon University in the Department of Electrical and Computer Engineering. During my time there, I worked on reinforcement learning with Prof. Ben Eysenbach and bandit algorithms with Prof. Gauri Joshi. I recieved my Bachelor’s degree in Electrical Engineering from IIT Madras, where I worked on reinforcement learning with Prof. L.A. Prashanth.
In my free time, I enjoy climbing, trail running and playing as well as watching football (soccer).
Updates:
- December 2023: Two papers accepted at AAAI-24! Distributional OPE for Slate Recommendations and Rethinking Eligibility Traces.
- November 2023: Gave a talk on off-policy evaluation under partial observability at A*STAR's Center for Frontier AI Research (CFAR).
- October 2023: Our work on model-based off-policy evaluation under partial observability was accepted at the RealML workshop at NeurIPS' 23.
- August 2023: Our work on distributional off-policy evaluation for slate recommender systems is up on arXiv.
- August 2022: Awarded the Robbin Popplestone Graduate Fellowship.
- May 2022: Started as a Research Scientist Intern as Adobe Research.