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Algorithms and Theory

This area focuses on the foundational and mathematical aspects of computer science. It encompasses research in algorithms (graph, probabilistic, parallel, data analysis), theory of computation, distributed computation, quantum computation, machine learning, game theory, computational biology, computational geometry, information theory and cryptography.

Research Faculty

Jianer Chen

  • Professor, Computer Science & Engineering
Jianer Chen

Tim Davis

  • Professor, Computer Science & Engineering
Tim Davis

Juan Garay

  • Professor, Computer Science & Engineering
Juan Garay

Anxiao (Andrew) Jiang

  • Professor, Computer Science & Engineering
Anxiao (Andrew) Jiang

John Keyser

  • Professor, Computer Science & Engineering
  • Graduate Advisor
John Keyser

Andreas Klappenecker

  • Professor, Computer Science & Engineering
Andreas Klappenecker

Dmitri Loguinov

  • Professor, Computer Science & Engineering
Dmitri Loguinov

Guni Sharon

  • Assistant Professor, Computer Science & Engineering
Guni Sharon

Sing-Hoi Sze

  • Associate Professor, Computer Science & Engineering
Sing-Hoi Sze

Shawna Thomas

  • Instructional Assistant Professor, Computer Science & Engineering
  • Undergraduate Advisor
  • Office: PETR 319 | Advising: EABA 104
  • Phone: 979-862-8877
  • Email: sthomas@tamu.edu
Shawna Thomas

Nate Veldt

  • Assistant Professor, Computer Science & Engineering
Nate Veldt

Courtesy Appointments

 

Nick Duffield

  • Professor, Electrical & Computer Engineering
  • Royce E. Wisenbaker Professor I
  • Director, Texas A&M Institute of Data Science
  • Affiliated Faculty, Computer Science & Engineering
Nick Duffield

J. Maurice Rojas

  • Adjunct Faculty, Computer Science & Engineering
  • Professor, Department of Mathematics, College of Arts and Science
J. Maurice Rojas

Courses Offered

CSCE 620/VIZA 720. Computational Geometry Credits 3. 3 Lecture Hours

Concrete algorithm design and analysis; abstract models to analyze the complexity of problems; NP-Completeness; approximation and probabilistic algorithms

Prerequisite: CSCE 311.
Cross Listing: VIZA 670/CSCE 620.

CSCE 626. Parallel Algorithm Design and Analysis. Credits 3. 3 Lecture Hours

Design of algorithms for use on highly parallel machines; area-time complexity of problems and general lower bound theory; application (of these concepts) to artificial intelligence, computer vision and VLSI design automation.

Prerequisite: CSCE 221.

CSCE 627. Theory of Computability. Credits 3. 3 Lecture Hours

Formal models of computation such as pushdown automata; Turing machines and recursive functions; unsolvability results; complexity of solvable results. 

Prerequisite: CSCE 433.

CSCE 629. Analysis of Algorithms. Credits 3. 3 Lecture Hours

Concrete algorithm design and analysis; abstract models to analyze the complexity of problems; NP-Completeness; approximation and probabilistic algorithms.

Prerequisite: CSCE 411.

CSCE 637. Complexity Theory. Credits 3. 3 Lecture Hours

Deterministic, non-deterministic, alternating and probabilistic computations; reducibilities; P, NP and other complexity classes; abstract complexity; time, space and parallel complexity; and relativized computation. 

Prerequisite: CSCE 627 or approval of instructor.

CSCE 640. Quantum Algorithms. Credits 3. 3 Lecture Hours

Introduction to the design and analysis of quantum algorithms; basic principles of the quantum circuit model; gives a gentle introduction to basic quantum algorithms; reviews recent results in quantum information processing. 

Prerequisite: CSCE 629 or approval of instructor.

CSCE 658. Randomized Algorithms. Credits 3. 3 Lecture Hours

Introduction to randomized algorithms; selected tools and techniques from probability theory and game theory are reviewed, with a view towards algorithmic applications; the main focus is a thorough discussion of the main paradigms, techniques, and tools in the design and analysis of randomized algorithms; a detailed analysis of numerous algorithms illustrates the abstract concepts and techniques. 

Prerequisite: Graduate classification.

CSCE 669. Computational Optimization. Credits 3. 3 Lecture Hours

Combinatorial theory of polytopes as a tool for the solution of combinatorial optimization problems; applications to max flow, matching and matroids; geometric interpretation of the results indicating the profound role that polyhedral combinatorics play in the design and complexity of approximation algorithms. 

Prerequisite: CSCE 629.

CSCE 711. Foundations of Modern Cryptography. Credits 3. 3 Lecture Hours

Perfectly secure encryption; pseudorandom functions and permutations; one-way functions; computational hardness; symmetric-key and public-key cryptography; more advanced cryptographic protocols. Rigorous quantifiable secure guarantees, based on precise mathematical definitions, reductions and provably secure protocols.  

Prerequisite: CSCE 411.