Current & Future Programs

Fall 2021

Aug. 18Dec. 17, 2021
This program brings together researchers in complexity theory, algorithms, statistics, learning theory, probability, and information theory to advance the methodology for reasoning about the computational complexity of statistical estimation problems.
Aug. 18Dec. 17, 2021
This program aims to develop a geometric approach to various computational problems in sampling, optimization, and partial differential equations.

Spring 2022

Jan. 11May 13, 2022
This program will bring together theoretical and applied researchers with the aim of understanding the complexity, optimization, and approximation questions underlying causal inference and discovery.
Jan. 11May 13, 2022
By bringing together researchers from machine learning, economics, operations research, theoretical computer science, and social computing, this program aims to advance the connections between learning theory, game theory, and mechanism design.

Summer 2022

May 23Jun. 24, 2022
This extended reunion will study fundamental questions on integer lattices and their important role in cryptography and quantum computation, bringing together researchers from number theory, algorithms, optimization, cryptography, and coding theory.
Jul. 5Aug. 5, 2022
This summer program aims to bring together computational and applied researchers to address key challenges in bioinformatics.

Fall 2022

Aug. 17Dec. 16, 2022
This program aims to develop algorithms for sequential decision problems under a variety of models of uncertainty, with participants from TCS, machine learning, operations research, stochastic control and economics.
Aug. 17Dec. 16, 2022
This program will bring together experts from various fields to study networks, from graph limits, to modeling and estimation, to processes on networks. Application areas include epidemics, spread of information and other economic and social processes.

Spring 2023

Jan. 10May 12, 2023
This program will bring together researchers in computational complexity, proof complexity, cryptography, and learning theory to make progress on fundamental problems in those areas using the framework of "meta-complexity" — i.e., complexity of computational tasks that are themselves about complexity.