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 omnipresent setting of partial observability.
Feel free to reach out in case you are interested in collaborating on related topics. I am also looking for internships for Summer 2024. Feel free to drop me an email if there’s a good fit!
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 and playing as well as watching football (soccer).
- 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.