Summer School 2024

We are hosting a summer school May 13-16, 2024 at Georgia Tech, on the topic of Probability, Algorithms, and Inference. The summer school is aimed primarily at PhD students and postdocs, but all are welcome to attend.

Travel support is available for PhD students.

The summer school is supported in part by the NSF grant “AF Small: Sampling and Optimization under Global Constraints”, PI’s Ewan Davies and Will Perkins.


The deadline to apply for travel support is February 15. The registration deadline is April 25

Tutorial Speakers

Shuangping Li (Stanford): On the binary perceptron
Shuangping Li

Shuangping Li is a Stein Fellow in the Department of Statistics at Stanford University. She received her PhD degree in Applied and Computational Mathematics in 2022 from Princeton University, where she was jointly advised by Professors Allan Sly and Emmanuel Abbé. Shuangping’s research interests span probability theory, statistics, and machine learning.

Marcus Michelen (UIC): Randomness and algorithms in sphere packing and independent sets
Marcus Michelen

Marcus Michelen is an Assistant Professor in the Department of Mathematics, Statistics, and Computer Science at the University of Illinois, Chicago (UIC). He received his Ph.D. in 2019 from the University of Pennsylvania where he was advised by Robin Pemantle.  He then was a postdoc at UIC where he was mentored by Will Perkins. His research is in probability and combinatorics with some intersections with statistical physics, theoretical computer science, and the geometry of polynomials.  Some of his recent research has focused on random matrices, random polynomials, and sphere packing/Gibbs point processes.

Guus Regts (Amsterdam): Graph polynomials and partition functions: zeros, algorithms and hardness
Guus Regts

Guus Regts obtained his PhD under the supervision of Lex Schrijver in 2013. He is currently an associate professor of mathematics at the University of Amsterdam. His research is centered around graph polynomials and partition functions with connections to computer science, statistical physics, and complex analysis and (complex) dynamical systems.

Ilias Zadik (Yale): Sharp thresholds in inference and implications on combinatorics and circuit lower bounds
Ilias Zadik

Ilias Zadik is Assistant Professor of Statistics and Data Science at Yale University. His research mainly focuses on the mathematical theory of statistics and its many connections with other fields such as computer science, probability theory, and statistical physics. His primary area of interest is the study of “computational-statistical trade-offs,” where the goal is to understand whether computational bottlenecks are unavoidable in modern statistical models or a limitation of currently used techniques. Prior to Yale, he held postdoctoral positions at MIT and NYU. He received his PhD from MIT in 2019.