Published papers: Constitutional classifiers: Defending against universal jailbreaks across thousands of hours of red teaming; Long-form factuality in large language models; Simple synthetic data reduces sycophancy in large language models; Symbol tuning improves in-context learning in language models; Larger language models do in-context learning differently.
Research Experience
Lead the deployment-robustness team at Anthropic; was a research engineer at Google DeepMind and Google Brain; interned as a software-engineering intern at Meta in 2022.
Education
Undergraduate at Stanford University, specializing in AI.
Background
An AI researcher focusing on deployment robustness; based in San Francisco.
Miscellany
Participated in several conference talks including Caltech AI Alignment, Generative AI Summit - Silicon Valley, Princeton AI Alignment, etc.