Department of Mathematics, MIT

77 Massachusetts Avenue

Cambridge, , MA 02139-4307

youngtak at mit.edu

I am a postdoc at MIT Mathematics hosted by Elchanan Mossel and Nike Sun. I am also a member of NSF/Simons program Collaborations on the theoretical foundations of deep learning. Previously, I received my Ph.D. at Stanford Statistics, where I was fortunate to be advised by Amir Dembo. I am broadly interested in probability theory and high-dimensional statistics. Here is my CV.

**Agreement and Statistical Efficiency in Bayesian Perception Models**[arXiv]

Yash Deshpande, Elchanan Mossel and Youngtak Sohn

*Preprint*.**One-step replica symmetry breaking of random regular NAE-SAT I**[arXiv]

Danny Nam, Allan Sly and Youngtak Sohn

*Preprint*.**The generalization error of max-margin linear classifiers: High-dimensional asymptotics in the overparametrized regime**[arXiv]

Andrea Montanari, Feng Ruan, Youngtak Sohn and Jun Yan

*Under major revision at Annals of Statistics*.**Exact Phase Transitions for Stochastic Block Models and Reconstruction on Trees**[arXiv]

Elchanan Mossel, Allan Sly and Youngtak Sohn

*Conference version to appear in proceedings of 55th STOC (2023)*.**One-step replica symmetry breaking of random regular NAE-SAT II**[arXiv][conference]

Danny Nam, Allan Sly and Youngtak Sohn

*Conference version in proceedings of 62nd FOCS (2021), pp. 310-318*.**Crisanti-Sommers formula and simultaneous symmetry breaking in multi-species spherical spin glasses**[arXiv][journal]

Erik Bates and Youngtak Sohn

*Communications in Mathematical Physics. 394 (2022), no. 3, 1101â€“1152*.**Free energy in multi-species mixed p-spin spherical models**[arXiv][journal]

Erik Bates and Youngtak Sohn

*Electronic Journal of Probability. 27(2022), paper no.52, 75pp*.**Replica symmetry breaking in multi-species Sherringtonâ€“Kirkpatrick model**[arXiv][journal]

Erik Bates, Leila Sloman and Youngtak Sohn

*Journal of Statistical Physics.174(2019), no.2, 333-350*.

**Teaching Assistantship at Stanford:**- Math 21, Calculus, Summer 2021
- STATS 310A, Theory of Probability 1, Fall 2020
- STATS 315B, Modern Applied Statistics: Data Mining, Spring 2020
- STATS 203, Introduction to Regression Models and Analysis of Variance, Winter 2020, Summer 2017
- STATS 110, Statistical Methods in Engineering and the Physical Sciences, Summer 2019
- STATS 218, Introduction to Stochastic Processes 2, Spring 2021, 2019, 2017
- STATS 217, Introduction to Stochastic Processes 1, Winter 2019, Summer 2018
- STATS 300B, Theory of Statistics, Winter 2018
- STATS 305A, Introduction to Statistical Modeling, Autumn 2017
- STATS 200, Inroduction to Statistical Inference, Winter 2017