Jonathan Frankle currently works as Chief Scientist at MosaicML, a startup dedicated to reducing the cost of training neural networks by changing the training algorithm itself. He leads the research team, which empirically studies the learning dynamics of practical neural networks, develops interventions that change the training algorithm to improve efficiency, and combines these speedup methods into recipes.

In the fall of 2023, he will be joining the faculty at Harvard as a member of the Computer Science Department.

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

Jonathan Frankle spends a portion of my time working on technology policy. In this capacity, he works closely with lawyers, journalists, and policymakers on topics related to AI. He currently work with the OECD to implement the AI Principles that they developed in 2019. He previously served as the inaugural Staff Technologist at the Center on Privacy and Technology at Georgetown Law, where he 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.