I'm a 4th year PhD candidate in CS at UC Berkeley, where I'm advised by Michael I. Jordan and Nika Haghtalab. I'm a Google PhD Fellow, a NSF Graduate Research Fellow, and affiliated with the Berkeley AI Research Lab (BAIR).
My current research focuses on enabling language models to reason in low-resource domains where verification is difficult, such as autonomously proving results in niche theoretical topics. I also enjoy manually proving things; in particular, my theoretical research studies the alignment and truthfulness challenges inherent in building multi-objective forecasting and learning systems.
I received my B.S. from Caltech in 2020 and previously interned with Google Research, Nvidia Research, and Salesforce Research.
Selected Works
(α-β) denotes when authors are ordered alphabetically.
- Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification
- Truthfulness of Decision-Theoretic Calibration Measures
- From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning
- On-Demand Sampling: Learning Optimally from Multiple Distributions
Selected Awards
- Google PhD Fellowship (2024)
- NSF Graduate Research Fellowship (2023)
- NeurIPS Best Paper Award (2022)