Hello! I just finished my Ph.D. at UC Berkeley. I will be an Assistant Professor in the Computer Science (CSD) and Machine Learning (MLD) departments at Carnegie Mellon University (CMU) in Fall 2024. I will also be a research scientist at Google DeepMind in Mountain View, starting September 2023.

Prospective students and collaborations. If you are interested in working / collaborating with me on topics in decision-making (and anything in machine learning, broadly), please feel free to reach out over email! I will be admitting PhD students at CMU in the 2023-2024 admission cycle. If you are an existing CMU PhD / MS student interested in working with me, please reach out as well.

[CV] (March 2023), [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.