Reza Gheissari
· Associate ProfessorNorthwestern University · Mathematics
Active 2013–2026
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About
Reza Gheissari received his PhD from New York University in 2019. After completing his doctoral studies, he held an appointment as a Miller Postdoctoral Fellow at UC Berkeley. He joined the faculty of Northwestern University in 2022. Gheissari works on probability theory and its applications. His research interests include the static and dynamic behavior of spin systems from statistical physics, as well as the relations of probability to sampling, optimization, and learning problems in high dimensions.
Research topics
- Artificial Intelligence
- Computer Science
- Algorithm
- Applied mathematics
- Geometry
- Mathematical analysis
- Mathematics
- Statistical physics
- Pure mathematics
Selected publications
Mean-field Potts and random-cluster dynamics from high-entropy initializations
Society for Industrial and Applied Mathematics eBooks · 2025-01-01 · 2 citations
book-chapterA common obstruction to efficient sampling from high-dimensional distributions with Markov chains is the multimodality of the target distribution because they may get trapped far from stationarity. Still, one hopes that this is only a barrier to the mixing of Markov chains from worst-case initializations and can be overcome by choosing high-entropy initializations, e.g., a product or weakly correlated distribution. Ideally, from such initializations, the dynamics would escape from the saddle poi…
On the tractability of sampling from the Potts model at low temperatures via random-cluster dynamics
Probability Theory and Related Fields · 2024-06-08 · 1 citations
articleSenior authorCorrespondingUniversality for high-dimensional stochastic gradient descent
Open MIND · 2026-01-01
otherOpen access1st authorCorrespondingA large family of high-dimensional statistical tasks share a common structure that their loss at a point in parameter space only depends on fixed-dimensional projections of the data (into  the directions of the parameter and ground truth vectors). This includes mixture classification problems and single and multi-index models with one or two-layer networks. When the data distribution is isotropic Gaussian, and the parameter is trained using online stochastic gradient descent (S…
Society for Industrial and Applied Mathematics eBooks · 2026-01-01
book-chapterSelf-correcting quantum memories store logical quantum information for exponential time in thermal equilibrium at low temperatures. By definition, these systems are slow mixing. This raises the question of how the memory state, which we refer to as the Gibbs state within a logical sector, is created in the first place.
Fast relaxation of the random field Ising dynamics
The Annals of Probability · 2026-01-01
article
Recent grants
Dynamics of Lattice and Mean-Field Spin Systems
NSF · $260k · 2023–2026
Frequent coauthors
- 53 shared
Eyal Lubetzky
New York University
- 21 shared
D. L. Stein
Karlsruhe Institute of Technology
- 18 shared
Charles M. Newman
New York University
- 18 shared
Gérard Ben Arous
New York University
- 18 shared
Aukosh Jagannath
University of Waterloo
- 13 shared
Antonio Blanca
Pennsylvania State University
- 9 shared
Yuval Peres
Beijing Institute of Mathematical Sciences and Applications
- 7 shared
Alistair Sinclair
University of California, Berkeley
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