Hello! I am a research scientist at Google DeepMind, based in Mountain View. I finished my Ph.D. from UC Berkeley in September 2023. I am interested in a number of topics (the most recent ones include):

[CV (a bit out of date)], [Google Scholar], [Twitter]

My research goal is to develop a “tool-box” to solve a variety of decision-making problems reliably, robustly and effectively in the real world. Towards this goal, I am interested in developing techniques and algorithms for sequential decision-making. Towards this goal, my past work has focused on developing methods for running reinforcement learning (RL) on static datasets and understanding and addressing challenges in using RL with deep neural networks. I am interested in taking my methods all the way to the real world and have been studying many applications (and I will continue to look out for interesting real-world applications!) – please check out our recent works on offline decision-making for chip design, real robot pre-training, and computational design! On-going works on crystal structure optimization in computational chemistry and promoter design in computational biology to come out soon. If you want to learn more about offline RL, please check out our NeurIPS tutorial.