Hello! I am a research scientist at Google DeepMind, based in Mountain View. I finished my Ph.D. from UC Berkeley in September 2023, and will start as an Assistant Professor in the Computer Science (CSD) and Machine Learning (MLD) departments at Carnegie Mellon University (CMU) in Fall 2024.
Prospective PhD students [New]. If you are interested in joining my group and working with me on topics in decision-making, reinforcement learning, and also more broadly machine learning and AI, please apply to the CMU PhD programs in the School of Computer Science (deadline: December 13, 2023)! I will be admitting students in the 2023-2024 cycle through the CMU admissions in Machine Learning, Computer Science, and Robotics. I am looking for a few students working in several topics, including but not limited to:
- reinforcement learning (RL) algorithms
- intersection of foundation models and decision making / reinforcement learning (both on using foundation models for decision making and developing decision-making and RL tools for building better foundation models)
- robotic learning and control
- Applications in science and computational design
Please mention my name in your application or statement of purpose if you are interested in joining my group.
If you are already an existing CMU PhD / MS / undergraduate student interested in working with me on research, please reach out to me on my CMU email with a brief description of what you are interested in and your research and academic background.
[CV (a bit out of date)], [Google Scholar], [Twitter]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.