Theory and Algorithms

Theoretical computer science studies the foundations computing from a rigorous and mathematical point of view. Core topics include models of computing, design and analysis of algorithms, data structures, protocols and mechanisms, and lower bounds.

Our faculty study algorithms in many diverse areas (computational geometry & topology, graphs, optimization, approximation, randomization), data structures, cryptography and secure computation, economics and computation, complexity theory, foundations of machine learning, and applications to several areas including operations research, computational biology, parallel & distributed computing, and networking to name a few. Our research, in addition to its fundamental importance, has many near and long term applications in Computer Science and beyond.

Faculty & Affiliate Faculty

Geometry, Parallel Algorithms, Computational Biology

Computational Geometry, Algorithms, Data Structures

Combinatorial Optimization, Integer Programming, Probabilistic Methods and Analysis, Randomized Algorithms  

Algorithms, Combinatorial Optimization, Mathematical Programming, Graphs

Graphs, Information Theory, Algorithms, Machine Learning

Combinatorial Optimization, Integer Linear Programming, Computational Biology

Computational Geometry and Topology, Algorithms

Pseudorandomness, Algebraic Computation, Computational Complexity

Algorithms, Game Theory, Fair Division

Algorithms for and Analysis of Networks and Distributed Systems

Computational Geometry, Geometric Approximation Algorithms

Optimization, Operations Research

Reinforcement Learning Theory, Machine Learning, Sample Complexity Analysis

Cryptography, Secure Computation, Zero-Knowledge, Differential Privacy

Algorithmic Game Theory, Mathematical Economics, Efficient Algorithms

Social Networks, Graph Algorithms, Applied Operations Research, Discrete Optimization, GPU-Accelerated Algorithms

Cryptography, Distributed Algorithms

Parallel Algorithms, Numerical Methods, Communication Cost Analysis, Quantum Computation

Machine Learning Theory

Model Checking, Logic, Cyberphysical Systems, Software, Security 

Graph Algorithms, Statistical Estimation, Heuristics for NP-Hard Optimization Problems, Experimental Algorithmics, Applications to Grand Challenges in Biology and Historical Linguistics

Machine Learning, Information Theory, Representation Learning

Machine Learning Theory, Operations Research, Combinatorial Optimization

Adjunct Faculty

Complexity Theory, Spectral Methods for Graph Algorithms 

Cryptography, Secure Multi-Party Computation 


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