Research Programs

The Summer Cluster on Quantum Computing will bring together researchers from academia and industry to explore topics from quantum complexity theory and cryptography to quantum algorithms, error-correction and fault tolerance, and benchmarking.

This program will study new approaches for analysis of functions over the Boolean hypercube and beyond, with the aim of making progress on fundamental problems in complexity, algorithms, and discrete mathematics.

This extended reunion is for long-term participants in the program Satisfiability: Theory, Practice, and Beyond, held in the Spring 2021 semester. It will provide an opportunity to meet old and new friends. Moreover, we hope that it will give everyone a chance to reflect on the progress made during the semester and since, and sketch in which directions the field should go in the future.

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.

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.

This cluster will aim to develop the theoretical foundations of deep learning, particularly the aspects of this methodology that are very different from classical statistical approaches.

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

Convenes an interdisciplinary group of scholars to develop firm theoretical and philosophical foundations for addressing some major issues concerning the interpretability of machine learning⁠–based models.

This cluster focuses on the growing concerns regarding the future of individuals, groups, societal institutions and values shaped, manipulated, and challenged by the imperatives of AI.

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.

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.

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.

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

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