Jake A. Soloff

I am an Assistant Professor of Statistics at the University of Michigan. My research focuses on the theory of statistical machine learning, with the goal of creating and understanding off-the-shelf tools that are both principled and broadly applicable.

I received my Ph.D. in Statistics from UC Berkeley in 2022, advised by Aditya Guntuboyina and Michael I. Jordan, and completed a postdoc at the University of Chicago with Rina Foygel Barber and Rebecca Willett.