I’ve spent summers working as a Data Engineering Intern @ SpaceX, Autonomous Vehicles Perception Intern @ NVIDIA, Motion Planning ML Intern @ Aurora, and Graduate Research Intern @ MSR.
In my free time, I do origami, hackathons, and lift. I’m a huge fan of birds (especially lovebirds), books (especially those by Murakami), and indie or alt. music (especially that of Radiohead).
If you’d be interested in working with me, feel free to shoot me an email!
Events & News
September 2022 - Two papers accepted to NeurIPS ‘22! One paper on minimax optimal online imitation learning and another paper on imitating an expert who has access to privileged information! Also, I was named as a top reviewer for the conference.
We derive the minimax optimal algorithm for imitation learning in the finite demonstration regime (i.e. an algorithm that is better than both online and offline IL in the worst case). Our work was published at NeurIPS 2022. [Website][Paper]
We construct a taxonomy for imitation learning algorithms, derive bounds for each class, construct novel reduction-based algorithmic templates that achieve these bounds, and implement simple elegant realizations with competitive emperical performance. Published at ICML 2021. [Website][Blog]