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I am currently a research scientist at Databricks.
Previously, I was a research scientist at Apple in Vladlen Koltun's research org, where I worked on reinforcement learning for LLM agents and multi-agent deep RL for autonomous driving. My PhD research focused on generative models that include stochastic structure and black box code execution, probabilistic inference in these models (e.g. sequential Monte Carlo, variational), and the compositionality of inference processes. I completed my PhD in EECS at MIT, where I was advised by Vikash Mansinghka and Josh Tenenbaum. During my PhD I created the Gen probabilistic programming system. My thesis is here. Prior to MIT, I was a technical lead at an early-stage molecular diagnostics startup backed by Sequoia Capital. I completed my MS in computer science at Stanford, where I researched machine learning for genomics. I completed my BS in EECS at UC Berkeley, where I worked with Pieter Abbeel on household robotics. My academic research has been funded by the NSF GRFP and the NDSEG fellowship.
