This program aims to advance our understanding of high-dimensional problems by focusing on the interplay between probability, geometry, and computation.

This program will bring together researchers in computer science, control theory, operations research and statistics to advance the theoretical foundations of reinforcement learning.

This program aims to foster interaction between theoreticians and practitioners to understand real-world efficient computation with a particular focus on the satisfiability problem for Boolean formulas.

Focusing on new developments in logic, automata, probabilistic modeling, games, and cyber-physical systems, this program aims to develop the theoretical foundations of computer systems.

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 will bring together theoretical and applied researchers with the aim of understanding the complexity, optimization and approximation questions underlying causal inference and discovery.