I currently work as Chief Scientist (Neural Networks) at MosaicML (recently acquired by Databricks), a startup dedicated to making it easy and cost-effective for anyone to train large-scale, state-of-the-art neural networks. I lead the research team.

I completed my PhD at MIT, where I empirically studied the behavior of practical neural networks with Prof. Michael Carbin. During my PhD, I investigated the properties of sparse neural networks that allow them to train effectively through my lottery ticket hypothesis. I previously earned my BSE and MSE at Princeton.

I spend a portion of my time working on technology policy. In this capacity work closely with lawyers, journalists, and policymakers on topics related to AI. I currently work with the OECD to implement the AI Principles that we developed in 2019. I previously served as the inaugural Staff Technologist at the Center on Privacy and Technology at Georgetown Law, where I contributed to a landmark report on police use of face recognition (The Perpetual Lineup) and co-developed a course on Computer Programming for Lawyers with Prof. Paul Ohm.