- About
- Events
- Calendar
- Graduation Information
- Cornell Tech Colloquium
- Student Colloquium
- Student Recognition
- 2020 Celebratory Event
- BOOM
- CS Colloquium
- SoNIC Workshop
- Conway-Walker Lecture Series
- Salton Lecture Series
- Seminars / Lectures
- Big Red Hacks
- Cornell University High School Programming Contest
- Game Design Initiative
- CSMore: The Rising Sophomore Summer Program in Computer Science
- Explore CS Research
- Research Night Fall 2020
- People
- Courses
- Research
- Undergraduate
- M Eng
- MS
- PhD
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
Robert Constable: Type theory and automated reasoning.
- Nika Haghtalab: Social and Economic Aspects of Machine Learning, Theory of Machine Learning, Algorithmic Economics, Artificial Intelligence, Algorithms, and Optimization
Joe Halpern: Reasoning about knowledge and uncertainty, distributed computing, causality, security, game theory.
Juris Hartmanis: Computational complexity 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.
Elaine Shi: Security, cryptography, programming languages, and systems, with applications to cryptocurrency, cloud computing, and privacy
David Shmoys: Approximation algorithms, computational sustainability.
Karthik Sridharan: Theoretical machine learning.
Noah Stephens-Davidowitz: Cryptography, lattices, geometry.
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.