I am an Associate Professor of Law at Columbia Law School. My research explores different topics at the intersection of AI and law. I have published across a range of venues, including ML conferences (ICML, NeurIPS, ICLR), law reviews (UPenn, Wisconsin, GW, Harvard JOLT), medical journals (NEJM, JAMA), and interdisciplinary computational legal venues (CSLaw, ICAIL). Here’s a representative sample of my recent work:
- There’s No Free Benchmark: An Institutional View of Legal AI Benchmarking (PNAS 2026)
- A Reasoning-Focused Legal Retrieval Benchmark (CSLaw 2025)
- AI Regulation Has Its Own Alignment Problem (GW L. Rev. 2024)
- Understanding Liability Risk from Using Health Care Artificial Intelligence Tools (NEJM 2024)
- Private Enforcement in the States (U. Pa. L. Rev. 2024)
- LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models (NeurIPS 2023)
I hold a PhD in computer science from Stanford; a JD from Stanford Law; a MS in Machine Learning from Carnegie Mellon University; and a BS in Computer Science from Stanford University. During graduate school I was supported by the Stanford Interdisciplinary Graduate Fellowship (SIGF) and the HAI Graduate Fellowship.