I am a final year PhD student in Computer Science at Stanford University, advised by Chris Ré. My research explores different topics in 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).
I am affiliated with the Hazy Research Lab, the RegLab, and the LIFTLab. I am grateful to be supported by the Stanford Interdisciplinary Graduate Fellowship (SIGF) and the HAI Graduate Fellowship. I hold a JD from Stanford Law, a MS in Machine Learning from Carnegie Mellon University, and a BSH in Computer Science from Stanford University.
Here’s a representative sample of my recent work:
- A Reasoning-Focused Legal Retrieval Benchmark (CS&Law 2025)
- AI Regulation Has Its Own Alignment Problem (GW L. Rev. 2024)
- Understanding Liability Risk from Using Health Care Artificial Intelligence Tools (NEJM 2024)
- Smoothie: Label Free Language Model Routing (NeurIPS 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)