I am a first year MS CS student at Stanford University advised by Shuran Song and Jiajun Wu. I will also be an AI Resident at 1X this summer working on humanoid robotics. Previously, I finished my BS EECS at UC Berkeley, where I was fortunate to be advised by Pieter Abbeel, Stephen James, and Xingyu Lin as a part of Berkeley's Robot Learning Lab.
My dream is for robots to become an everyday household occurrence. Towards this, I aim to answer two questions:
My recent interests are in capturing priors from actionless videos for robot learning, such as motion and skills. Long term, I hope to leverage perspectives from cognitive science to inform how robots can learn from and like humans.
We learn to predict the future trajectories of arbitrary points by pre-training on actionless videos. Using these trajectories for downstream policy learning, we demonstrate sample-efficient learning and cross-embodiment knowledge transfer.
We extract dense features from pre-trained networks to learn generalizable manipulation skills. This shows improvements on categorical generalization against paradigms such as naively using pre-trained representations.
We use photorealistic simulation to learn a segmentation model and train a navigation policy with RL in the learned segmentation space. We deploy zero-shot to a real vehicle.
CS 229: Machine Learning: Winter 2024
CS 189: Introduction to Machine Learning: Spring 2023
CS 61A: Structure and Interpretation of Computer Programs: Fall 2022, Spring 2022 (Head TA), Fall 2021
As an undergrad, I spent the majority of my time outside of research helping to build, organize, and lead Machine Learning at Berkeley (ML@B), serving as the organization's president in Fall 2022. We presented a white paper about our structure and initiatives at the NeurIPS 2022 Broadening Research Collaborations in ML Workshop; you may find a preprint here.
I like to think about how to best teach, learn, and optimize for fulfillment. Shoot me an email or DM if you'd like to chat :)