About Me

I am a 5th year PhD student at UC Berkeley, jointly advised by Jiantao Jiao and Kannan Ramchandran, affiliated with the BLISS and BAIR labs. I was previously an intern with Nevena Lazic and Dong Yin at Deepmind and with Ravishankar Krishnaswamy at MSR.

I work on a variety of topics in the theory of machine learning with a general focus on the statistical and computational aspects of adaptive decision making problems and reinforcement learning. More recently, I have been interested in the application of these techniques in pushing our understanding of large language models. My research has largely focused on using mathematical frameworks to provide intuitions missing in existing empirical approaches.

I was previously an organizer of the BLISS and CLIMB seminars at Berkeley. Shoot me an email if you are interested in giving a talk at either venue!

In a previous life, I was a dual degree student at the Department of Electrical Engineering, IIT Madras. I am fortunate to have had Andrew Thangaraj as my thesis advisor and to have worked closely with Rahul Vaze.

Updates

Feb 2024: I presented a poster on our work on tokenization at ITA in sunny San Diego.

July 2023: I passed my qualifying exam! Thanks to my committee members, Profs. Jiantao Jiao, Kannan Ramchandran, Sasha Rakhlin, and chaired by Prof. Mike Jordan.

April 2023: I taught a guest lecture for Statistical Machine Learning (ECE 6254) at Georgia Tech.

April 2023: I presented our work on pruning for matrix sensing at Georgia Tech.

January 2023: I presented our recent work on nonlinear bandits at Google Research India and MSR India.

November 2022: I will be at Neurips 2022. Hit me up if you will be around!

October 2022: I gave a talk on nonlinear bandits at the WNCG group at UT Austin.

October 2022: I will be participating in AIDS LifeCycle 2023. You can support me by clicking here. Every little bit counts!

Publications and Preprints

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