About me

Hey, I’m a PhD Candidate at MIT. My research focuses on the intersection of AI and policy: responsibly training, evaluating, and governing general-purpose AI systems. I lead the Data Provenance Initiative, led the Open Letter on A Safe Harbor for Independent AI Evaluation & Red Teaming, and have contributed to training models like Bloom, Aya, and Flan-T5/PaLM. I’m thankful for the recognition my research has received: Best Paper Awards from ACL 2024, NAACL 2024, as well as coverage by the NYT, Washington Post, Atlantic, 404 Media, Vox, and MIT Tech Review.

Prior:

• Google Brain Student Researcher (2022), collaborating with Barret Zoph, Jason Wei.
• Applied ML at Apple.
• Research at Stanford NLP lab, advised by Chris Manning and Danqi Chen.
• Research at Salesforce Research, supervised by Caiming Xiong and Richard Socher.
• Stanford University: Economics BA, Computer Science MS, rowing, soccer, creative writing.

See my full resume here, and full list of publications here.

Recent News

• 2025.01: Core writing team for the International AI Safety Report.

• 2024.12: Lead organizer for The Future of Third-Party AI Evaluation Workshop, recorded here.

• 2025.10: Multimodal Data Provenance accepted to ICLR 2025. Covered by MIT Tech Review.

• 2024.08: Aya Model wins Best Paper Award at ACL 2024.

• 2024.08: Consent in Crisis accepted to NeurIPS 2024. Covered by the NYT, 404 Media, Vox, and Yahoo! Finance.

• 2024.07: A Pretrainer’s Guide to Training Data wins Outstanding Paper Award at NAACL 2024.

• 2024.07: 3 Oral and 1 Spotlight paper accepted to ICML 2024: (1) Safe Harbor, (2) Societal Impact of Open Foundation Models, (3) AI Autonomous Weapons Risk Geopolitical Instability, and (4) Data Authenticity, Consent, and Provenance for AI Are All Broken: What Will It Take to Fix Them?.

• 2024.06: The Data Provenance Initiative was awarded the Mozilla Data Futures Lab grant and wins the MIT Generative AI Impact Award, funded for $70,000. Presented at MozFest 2024.