Theoretical Foundations

Research in theoretical foundations formally defines both the types of problems that can be solved using a computer and the quality of their solutions. Computers are limited by space and time. The optimal solution to a computational problem often lies outside these limits, thus an approximate solution must be computed. Methods developed in this area define the plausibility of an optimal solution, the quality of the approximate solution, and the resources necessary to find each, thus leading the way to better utilization of a computer's resources or those of multiple computers in parallel. Specific research in this area encompasses a broad range of foundational topics in computer science including computational learning theory, complexity theory, algorithm and data structure design, parallel algorithms, geometric computing, cryptography, computational logic, programming languages theory, and matrix computations. Several group members are also engaged actively in leveraging their research into various application areas.

 

Faculty

Dan Boley headshot
Distinguished University Teaching Professor, Director of Graduate Studies for Data Science
Office
6-209 Keller Hall
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Favonia headshot
Assistant Professor
Office
6-209 Keller Hall
Phone
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Nicholas Hopper thumbnail
Professor, Director of Undergraduate Studies
Office
4-211 Keller Hall
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Ravi Janardan headshot
Professor
Office
6-217 Keller Hall
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George Karypis headshot
Distinguished McKnight University Professor
Office
483 Walter Library
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Vipin Kumar headshot
Regents Professor, William Norris Land Grant Chair in Large-Scale Computing
Office
5-225C Keller Hall
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Gopalan Nadathur headshot
Professor
Office
6-215 Keller Hall
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Yousef Saad headshot
CSE Distinguished Professor, William Norris Land Grant Chair in Large-Scale Computing
Office
5-225B Keller Hall
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Ju Sun headshot
Assistant Professor
Office
5-225E Keller Hall
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Eric Van Wyk headshot
Professor
Office
6-203 Keller Hall
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