I research a variety of topics in reliable, scalable machine learning. I'm a co-founder of the GenLaw Center, a postdoctoral researcher at Microsoft Research, and a postdoctoral affiliate at Stanford HAI, RegLab, and CRFM working with Percy Liang and Dan Ho. I am also a Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University. In 2026, I'll be appointed as an Assistant Professor of Computer Science at Yale University. I'll also be affiliated with the Information Society Project at Yale Law School, the Center for Algorithms, Data, and Market Design, and the Institute for Foundations of Data Science.
My contributions span uncertainty estimation, privacy and security of generative-AI systems, distributed training, hyperparameter optimization, and model selection. I also do work in tech policy and law, and spend a lot of time finding ways to effectively communicate the capabilities and limits of AI/ML to interdisciplinary audiences and the public. My research has received spotlights, orals, and best-paper accolades at top AI/ML and computing venues, including NeurIPS, ICML, AAAI, and AIES. Law collaborations on copyright and Generative AI have been lauded as "landmark" work among technology law scholars and the popular press. I occasionally write about machine learning and related topics here.
In the past I interned at Microsoft Research and at Google Research, and was named a "Rising Star in EECS" by MIT. My doctoral work was generously supported by the John T. and Catherine D. MacArthur Foundation through AIPP. Prior to my research career, I worked for several years as a software engineer at companies both (really) big and (really) small. I specialized in designing, building, and monitoring large-scale backend data-processing systems.
(I am not recruiting students for Fall 2025 -- my appointment at Yale starts in 2026.)
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