I work on AI policy at METR, an AI evaluations nonprofit based in Berkeley, California. I help support major AI developers with designing frontier safety policies and provide input on public policy.
Before METR, I was a software engineer at Stripe, working on backend infrastructure and the occasional LLM application. I coauthored a paper on AI deception that was covered in The Guardian and MIT Technology Review.
RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts. Accepted to the International Conference on Machine Learning, 2025.
Common Elements of Frontier AI Safety Policies. METR. 2024.
Response to U.S. AI Safety Institute Draft “Managing Misuse Risk for Dual-Use Foundation Models”. METR. 2024.
Response to NIST Draft “AI Risk Management Framework: Generative AI Profile”. METR. 2024.
The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning. Featured in Time. International Conference on Machine Learning, 2024.
AI deception: A survey of examples, risks, and potential solutions. Featured in The Guardian and MIT Technology Review. Patterns, 2024.
Inverse Design of Nanophotonic Structures Using a Hybrid Dimensionality Reduction Technique. Frontiers in Optics. 2020.