Regular biography
Reza Gheissari is an Associate Professor in the Department of Mathematics at Northwestern University. He received his PhD from New York University in 2019 and served as a Miller Postdoctoral Fellow at UC Berkeley before joining Northwestern in 2022. His research focuses on probability theory and its applications, particularly the static and dynamic behavior of spin systems from statistical physics, as well as connections between probability and sampling, optimization, and learning problems in high dimensions.
Scholar-generated biography
Reza Gheissari is a researcher at Northwestern University, focusing on high-dimensional statistical inference, stochastic gradient descent, and spin glass dynamics. His work explores algorithmic thresholds for tensor PCA, mixing times of critical Potts models, and concentration inequalities for polynomials of contracting Ising models. He investigates the spectral gap of spherical spin glass dynamics and the behavior of Ising models under various boundary conditions. His research also includes the analysis of random-cluster dynamics and the mixing properties of Swendsen–Wang dynamics. Gheissari's contributions span both theoretical and applied aspects of statistical mechanics and machine learning.