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Artificial Intelligence, Intelligent Systems, Machine Learning, Natural Language Processing

Artificial intelligence research aims at building intelligent computer programs that can make human-like predictions and decisions while being mindful to ethical and societal implications. When the systems are trained on real data, they can be used to perform tasks that otherwise require human manual work, including autonomous driving, robotics and medical diagnosis, etc.

Research Faculty

Yoonsuck Choe

  • Professor, Computer Science & Engineering
Yoonsuck Choe

Wenhui Chu

  • Instructional Assistant Professor, Computer Science & Engineering
  • Office: SAGC 705 (Galveston)
  • Phone: 409-740-5516
  • Email: chuw1@tamu.edu
Wenhui Chu

Tomer Galanti

  • Assistant Professor, Computer Science & Engineering
Tomer Galanti

Ricardo Gutierrez-Osuna

  • Professor, Computer Science & Engineering
Ricardo Gutierrez-Osuna

Tracy Anne Hammond

  • Professor, Computer Science & Engineering
Tracy Anne Hammond

Kuan-Hao Huang

  • Assistant Professor, Computer Science & Engineering
Kuan-Hao Huang

Ruihong Huang

  • Associate Professor, Computer Science & Engineering
Ruihong Huang

Thomas R. Ioerger

  • Professor, Computer Science & Engineering
Thomas R. Ioerger

Shuiwang Ji

  • Professor, Computer Science & Engineering
  • Truchard Family Chair
  • Presidential Impact Fellow
  • Chancellor EDGES Fellow
Shuiwang Ji

Zbigniew Leyk

  • Professor of Practice, Computer Science & Engineering
Zbigniew Leyk

Robin Murphy

  • Raytheon Professor, Computer Science & Engineering
Robin Murphy

Abdullah Muzahid

  • Associate Professor, Computer Science & Engineering
Abdullah Muzahid

Guni Sharon

  • Assistant Professor, Computer Science & Engineering
Guni Sharon

Dylan Shell

  • Professor, Computer Science & Engineering
Dylan Shell

Cheng Zhang

  • Assistant Professor, Computer Science & Engineering
Cheng Zhang

Yi Zhou

  • Associate Professor, Computer Science & Engineering
Yi Zhou

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

Courses Offered

CSCE 625. Artificial Intelligence. Credits 3. 3 Lecture Hours
Basic concepts and methods of artificial intelligence; Heuristic search procedures for general graphs; game playing strategies; resolution and rule-based deduction systems; knowledge representation; reasoning with uncertainty.
Prerequisite: CSCE 221

CSCE 631. Intelligent Agents Parallel Algorithm Design and Analysis. Credits 3. 3 Lecture Hours
On the design and implementation of Intelligent Agents and coordination mechanisms among multiple agents, ranging from theoretical principles to practical methods for implementation. 
Prerequisite: CSCE 420 or CSCE 625

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, and complexity of solvable results.
Prerequisite: CSCE 433

CSCE 636. Neural Networks. Credits 3. 3 Lecture Hours
Basic concepts in neural computing; functional equivalence and convergence properties of neural network models; associative memory models; associative, competitive and adaptive resonance models of adaptation and learning; selective applications of neural networks to vision, speech, motor control and planning; neural network modeling environments. 
Prerequisite: MATH 304 and MATH 308 or approval of instructor.

CSCE 639/MEEN 676. Fuzzy Logic and Intelligent Systems. Credits 3. 3 Lecture Hours
Introduces the basics of fuzzy logic and its role in developing intelligent systems; topics include fuzzy set theory, fuzzy rule inference, fuzzy logic in control, fuzzy pattern recognition, neural fuzzy systems and fuzzy model identification using genetic algorithms.  
Prerequisite: CSCE 625 or approval of instructor
Cross Listing: MEEN 676/CSCE 639

CSCE 644. Cortical Networks. Credits 3. 3 Lecture Hours
The architecture of the mammalian cerebral cortex; its modular organization and its network for distributed and parallel processing; cortical networks in perception and memory; neuronal microstructure and dynamical simulation of cortical networks; the cortical network as a proven paradigm for the design of cognitive machines.
Prerequisite: CSCE 420 or CSCE 625 and CSCE 636 and graduate classification