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Youngtak Sohn is an Assistant Professor in the Department of Applied Mathematics at Brown University. His research primarily focuses on probability theory, mathematical statistics, theoretical computer science, and statistical physics. Currently, his research interests include high-dimensional statistics, random constraint satisfaction problems, statistical inference on random graphs, and spin glass theory. Prior to his role at Brown, he was a postdoctoral researcher in the Mathematics Department at the Massachusetts Institute of Technology (MIT) and a member of the NSF/Simons program on the Theoretical Foundations of Deep Learning. He obtained his PhD in Statistics from Stanford University and earned his Bachelor of Science in Mathematics from Seoul National University.
Department: Department of Economics