I currently work as Chief Scientist (Neural Networks) at MosaicML (now a part of 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, which empirically studies the learning dynamics of practical neural networks, develops interventions that change the training algorithm to improve quality and efficiency, and combines these methods into training recipes.

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.

You can find my full academic CV here.