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The theory of computing is the study of efficient computation, models of computational processes, and their limits. Research at Cornell spans all areas of the theory of computing and is responsible for the development of modern computational complexity theory, the foundations of efficient graph algorithms, and the use of applied logic and formal verification for building reliable systems. In keeping with our tradition of opening new frontiers in theory research, we have emerged in recent years as a leader in exploring the interface between computation and the social sciences.
In addition to its depth in the central areas of theory, Cornell is unique among top research departments in the fluency with which students can interact with faculty in both theoretical and applied areas, and work on problems at the critical juncture of theory and applications.
Faculty
Jayadev Acharya: Information theory, machine learning, and algorithmic statistics.
Siddhartha Banerjee: Stochastic Modeling, Design of Scalable Algorithms, Matching Markets and Social Computing, Control of Information-Flows, Learning and Recommendation.
Eshan Chattopadhyay : Randomness and Computation, Computational Complexity theory, Cryptography.
Robert Constable: Type theory and automated reasoning.
Ziv Goldfeld: Information theory, mathematical statistics, optimal transport, and statistical learning theory.
Joe Halpern: Reasoning about knowledge and uncertainty, distributed computing, causality, security, game theory.
John Hopcroft: Algorithms, information capture and access, random graphs and spectral methods.
Bobby Kleinberg: Algorithms, game theory, learning, and networks.
Jon Kleinberg: Algorithms, social and information networks.
Dexter Kozen: Computational complexity, program logic and semantics, computational algebra.
Rafael Pass: Cryptography and its interplay with computational complexity and game theory.
Thomas Ristenpart: Cryptography, computer security, technology abuse.
David Shmoys: Approximation algorithms, computational sustainability.
Karthik Sridharan: Theoretical machine learning.
Noah Stephens-Davidowitz: Theory, lattices, geometry, cryptography.
Wen Sun: Theoretical Reinforcement Learning and Machine Learning.
Eva Tardos: Algorithms, algorithmic game theory.
Madeleine Udell: Optimization and machine learning for large scale data analysis and control.
David Williamson: Approximation algorithms, information networks.
Christina Lee Yu: Algorithms, high dimensional statistics, sequential decision making, machine learning.
Anke van Zuylen: Algorithms, combinatorial optimization.
Courses
* offered Spring '22