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 is on understanding how to effectively apply language models to problems where inference-time compute budgets are large (>1B tokens per problem) and verification is non-trivial. Previously, I worked at the intersection of machine learning and game theory, studying the alignment challenges inherent in multi-objective forecasting and multi-distribution learning.
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)