Neel Guha
PhD, Computer Science, Stanford University
JD, Stanford Law School
nguha [AT] cs.stanford.edu
I am a fifth year JD-PhD student in Computer Science at Stanford University (advised by Chris Ré). I'm a part of the Hazy Research Lab, Stanford Center for Research on Foundation Models, and RegLab. I graduated with a MS in Machine Learning from Carnegie Mellon University ('19) and a BS (with Honors) in Computer Science from Stanford University ('18). I am grateful to be supported by the Stanford Interdisciplinary Graduate Fellowship (SIGF) and the HAI Graduate Fellowship.
My research lies at the intersection of artificial intelligence/machine learning (AI/ML) and law. Specifically, I explore:
- What types of legal reasoning tasks can large language models (LLMs) perform effectively? I’ve contributed to the development of benchmarks that evaluate LLM performance on diverse legal reasoning and data tasks. These include LegalBench, CaseHOLD, and LoCo.
- How can LLM performance be improved without labeled data or human supervision? I investigate techniques that leverage auxiliary information (e.g., embeddings) to enhance model performance in unsupervised or weakly supervised settings. Examples of this work include Smoothie, Embroid, and Bootleg.
- How can machine learning advance our understanding of the law and legal institutions? Empirical legal research often faces bottlenecks due to the cost and time of manual data coding. My work explores how machine learning can help construct large-scale datasets more efficiently, such as measuring private enforcement across states and building an annotated database of state statutes.
- How should we govern AI? I examine how AI systems, particularly in sensitive domains like healthcare, can be effectively regulated. My research includes frameworks for assessing liability for medical AI and analyzing the technical and institutional trade-offs associated with traditional regulatory interventions, such as disclosure and licensing.
Recent News
November 2024: Diego Zambrano and I have a short piece out in the Wisconsin Law Review talking about a new empirical project that uses LLMs to build an annotated database of state statutes! October 2024: Michelle Mello and I appeared on the Stanford Legal Podcast (hosted by Professor Pam Karlan and Professor Rich Ford) to talk about our work on AI liability in healthcare! September 2024: Our work on learning unsupervised routers for LLMs accepted to NeurIPS 2024! July 2024: Excited to contribute to Open Problems in Technical AI Governance! May 2024: Excited to contribute a chapter on benchmarking language models for legal applications to The Oxford Handbook on the Foundations and Regulation of Generative AI (OUP, 2024). May 2024: Two papers accepted to ICML 2024: (1) Prospectors, and (2) Long-Context Retrievers. January 2024: Understanding Liability Risk from Using Health Care Artificial Intelligence Tools out in The New England Journal of Medicine (with Michelle Mello). January 2024: Private Enforcement in the States out in University of Pennsylvania Law Review (with Diego Zambrano, Austin Peters, and Jeffrey Xia).Website design and template by Martin Saveski.