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.

This program aims to develop a geometric approach to various computational problems in sampling, optimization, and partial differential equations.

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.

By bringing together reseachers 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.

This summer program aims to bring together computational and applied researchers to address key challenges in bioinformatics.

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.

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.