Research Programs

This short-format program will focus on the design of markets for online platforms, exploiting advances in theoretical computer science, economics and operations research.

This program will bring together researchers from academia and industry to develop empirically-relevant theoretical foundations of deep learning, with the aim of guiding the real-world use of deep learning.

This program will focus on emerging connections between the analytic theory of multivariate polynomials (sometimes called "the geometry of polynomials") and theoretical computer science as well as related fields such as combinatorics, probability, statistical physics, optimization and real algebraic geometry. 

This program aims to promote research on the theoretical foundations of data privacy, as well as on applications in technical, legal, social and ethical spheres.

This program will bring together leading researchers in computational complexity theory to tackle fundamental questions on the capabilities and limitations of various models of computation.

Taking inspiration from the areas of algorithms, statistics, and applied mathematics, this program aims to identify a set of core techniques and principles for modern Data Science. 

This program aims to rekindle the historical affinity between the fields of Neuroscience and Theoretical Computer Science, in order to attack some of the most important current problems in understanding the structure and function of the brain.

The program will bring together experts in physical science, engineering and societal systems with mathematical and computational scientists to work on a wide range of problems involving real-time discovery and inference.

The last decade has seen an emerging confluence of ideas from discrete and continuous optimization, leading to several significant breakthroughs.  This program will bring together researchers from both the discrete and continuous optimization communities in order to stimulate further interaction at this interface.

This program aims to extend the reach and impact of CS theory within machine learning, by formalizing basic questions in developing areas of practice, advancing the algorithmic frontier of machine learning, and putting widely-used heuristics on a firm theoretical foundation.

This program will gather researchers in theoretical computer science, combinatorics and number theory to develop a unified approach to notions of pseudorandomness and their applications.

Classical approaches to algorithm design are both over-pessimistic (in making worst-case assumptions about the input) and over-optimistic (in assuming that the input is completely specified).  This program explores alternatives to worst-case analysis of algorithms, as well as new methods for addressing uncertainty in instance specifications, with reference to a broad range of applications in learning, optimization and control.

Logic in computer science has long been associated with two main themes: the interaction of logic with algorithms and complexity theory, and the semantics of programs and processes. This program will bring together researchers from both ends of this spectrum, with the aim of bridging this decades-old divide.

Research on computational aspects of counting problems and partition functions has recently seen significant advances. This program aims to better understand the computational complexity of both exact and approximate counting problems, and their relationship to phase transitions in combinatorics and statistical physics.

This program aims to unify data-driven and theoretical developments in bioinformatics by bringing together leaders and young scientists with strong interests in the algorithmic, methodological and theoretical aspects of computational biology.

Economics and computer science have developed a remarkable number of points of contact over the past two decades.  This program aims to build on these existing interactions in order to identify and make progress on a new generation of research problems at the intersection of the two fields.