Published multiple papers on enhancing the security/privacy/robustness of ML, improving large language models, and their intersection. Notable works include 'Scalable Extraction of Data from (Production) Language Models' and 'The False Promise of Imitating Proprietary LLMs'.
Research Experience
At OpenAI, works on safety and capabilities research, heavily involved in post-training and alignment for major models including GPT-4o mini, o1 and o1-preview, o1-mini, Instruction Hierarchy, o3 and o4-mini, and Deep Research.
Education
PhD - University of California, Berkeley, Advisors: Dan Klein and Dawn Song
Background
A researcher at OpenAI, working to make the next-generation of LLMs more safe, robust, and private. Previously, he did a PhD at UC Berkeley.