I'm a research scientist at OpenAI where I work on LLMs and reasoning.
Previously, I worked on a mix of theoretical machine learning, statistics, and game theory. My research has been recognized with a Neurips outstanding paper award and the Google PhD fellowship.
I received my PhD in computer science from UC Berkeley, where I was advised by Michael I. Jordan and Nika Haghtalab. Before my PhD, I received my B.S. in computer science from Caltech.
Selected Works
(α-β) denotes when authors are ordered alphabetically.
- On-Demand Sampling: Learning Optimally from Multiple Distributions
- Truthfulness of Decision-Theoretic Calibration Measures
- Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification
Selected Awards
- Google PhD Fellowship (2024)
- NSF Graduate Research Fellowship (2023)
- NeurIPS Best Paper Award (2022)