About Me

I’m a joint JD-PhD (Computer Science) candidate at Stanford University where I’m lucky enough to be advised by Dan Jurafsky. I’m also an OpenPhilanthropy AI Fellow and a Graduate Student Fellow at the Regulation, Evaluation, and Governance Lab. At Stanford Law School, I co-led the Domestic Violence Pro Bono Project, worked on client representation with the Three Strikes Project, and contributed to the Stanford Native Law Pro Bono Project. Previously, I was lucky enough to be advised by David Meger and Joelle Pineau for my M.Sc. at McGill University and the Montréal Institute for Learning Algorithms.

I also spent time as a Software Engineer and Applied Scientist at Amazon AWS/Alexa, worked with Justice Cuéllar at the California Supreme Court, am a part-time researcher with the Internal Revenue Service Research, Applied Analytics and Statistics Division, and am a Technical Advisor at the Institute for Security+Technology.

My research focuses on the intersection of AI and Law, in particular creating robust decision-making systems and regulatory frameworks for safe ML deployments. My goals are three-fold:

1. AI for Law and Government
   • Using AI to make legal systems and governments more accessible, efficient, and fair.
2. Responsible AI & Law for AI
   • Developing rigorous techniques for ethical, safe, sustainable, and reproducible AI systems.
   • Developing new regulatory and legal frameworks to prevent harmful deployments of AI.
3. Fundamental Research in Decision-Making Systems
   • Creating new ML methods for applications that are beneficial to society.
   • Developing new methods to better understand the dynamics and behavior of ML.
   • Developing regulation- and policy-aware ML systems.

Some of my work has received coverage by TechCrunch, Science, The Wall Street Journal, and more.

From the law side, my work intersects the most with administrative law, but sometimes involves antidiscrimination law, antitrust law, intellectual property, and civil procedure, among others. I’ve also dabbled in other fields like economics. Overall, I’m interested in a wide range of other research, policy, and legal work, so get in touch if you’d like to collaborate!

Latest News

• I will be presenting our draft working paper “Algorithmic Rulemaking v. Algorithmic Guidance” (with Mark Krass) at WeRobot 2022!
• My recent panel discussion on AI and sustainability received some coverage at TechTarget!
• Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset is out! Check it out, as well as the accompanying HAI Blog Post.
• Vulnerabilities in Discovery Tech is now officially in the Harvard Journal of Law & Technology (2022)! We investigate how ML is used in discovery proceedings, identify potential risks and vulnerabilities, and suggest a path forward for ensuring robust use of ML in civil litigation.
• Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection appeared at the Adaptive Experimental Design and Active Learning in the Real World Workshop @ ICML 2022 and the 12th Annual IRS/TPC Joint Research Conference on Tax Administration. It’s an example of RL (okay, well bandits) used in the public sector in a real world setting, taking into account real policy objectives in an under-explored setting of algorithms. You can see my talk on it here.