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Jake Soloff is an Assistant Professor in the Department of Statistics at the University of Michigan. His research examines the theoretical and philosophical foundations of statistical machine learning, aiming to develop methods that are principled and broadly applicable. He received his PhD in Statistics from the University of California, Berkeley in 2022, where he was advised by Aditya Guntuboyina and Michael Jordan. Following his PhD, he completed a postdoctoral fellowship at the University of Chicago, working with Rina Foygel Barber and Rebecca Willett. His current work focuses on enhancing statistical methods in various practical applications.
Department of Electrical Engineering and Computer Science