My research focuses on aligning machine learning, law, and policy for responsible real-world deployments. This involves two major tracks. First, I develop new machine learning methods that are useful for real world partnerships for public good. For example, we partnered with the Internal Revenue Service to better understand and improve algorithms involved in their tax administration programs, making them more efficient, legally-complaint, and equitable. Second, I develop both technical and regulatory approaches to ensure that AI is used for the benefit of all in a safe and responsible manner. This includes work on AI safety, but also core work on legal doctrine and policy. Some of my work has received coverage by TechCrunch, Science, New York Times, The Wall Street Journal, Bloomberg, and more.