About
Youngtak Sohn is an Assistant Professor in the Division of Applied Mathematics at Brown University since July 2024. Previously, he was a postdoctoral researcher at MIT Mathematics, mentored by Elchanan Mossel and Nike Sun through the NSF/Simons Collaboration on Theoretical Foundations of Deep Learning. He earned his PhD in Statistics from Stanford University (2021) under Amir Dembo and a BSc in Mathematics from Seoul National University (2016).
His research focuses on probability theory and its intersections with mathematical statistics, theoretical computer science, and statistical physics. Key areas include high-dimensional statistics, random constraint satisfaction problems, spin glass theory, and the analysis of stochastic networks. Recent work explores phase transitions in estimation problems, universality of max-margin classifiers, and solution geometry of random constraint satisfaction problems.
He has taught courses at Brown (APMA 2640) and MIT (Theoretical Computer Science seminar), and served as a teaching assistant for numerous Stanford courses including stochastic processes, statistical inference, and regression analysis.
