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Frederic Koehler is an Assistant Professor in the Department of Statistics at the University of Chicago, where he is also part of the Data Science Institute (DSI). He earned his PhD in Mathematics and Statistics from the Massachusetts Institute of Technology, co-advised by Ankur Moitra and Elchanan Mossel. Prior to his current role, he was a Postdoctoral Fellow at Stanford University and worked as a research fellow at UC Berkeley's Simons Institute, focusing on Computational Complexity and Statistical Inference. His research interests center on computational learning theory, particularly topics related to probability theory, high-dimensional statistics, optimization, and aspects of statistical physics. He has a strong foundation in mathematics, having completed his undergraduate studies at Princeton University. Koehler's work aims to advance the understanding of learning and inference in graphical models, contributing significantly to the fields of statistics and data science.
Stanford University • Stanford, CA
Conducted research in computational learning theory.
UC Berkeley’s Simons Institute • Berkeley, CA
Engaged in research on computational complexity and statistical inference.
Department of Philosophy