Dr. Donald C. Wunsch II

Dr. Donald C. Wunsch II

Mary Finley Missouri Distinguished Professor, Missouri University of Science and Technology
Director, Kummer Institute Center for Artificial Intelligence and Autonomous Systems

Biography

Donald C. Wunsch II is the Mary Finley Missouri Distinguished Professor and Director, Kummer Institute Center for Artificial Intelligence and Autonomous Systems at Missouri University of Science and Technology (Missouri S&T).

He is also Director of the Applied Computational Intelligence Laboratory.

Earlier employers were: Texas Tech University, Boeing, Rockwell International and International Laser Systems.

Research Interest

Clustering; neural networks; reinforcement learning; approximate dynamic programming; adaptive dynamic programming.

Education

  • Ph.D., Electrical Engineering – University of Washington (Seattle)
  • Executive MBA – Washington University in St. Louis, M.S.
  • Applied Mathematics – University of Washington (Seattle)
  • B.S., Applied Mathematics – University of New Mexico (Albuquerque)
  • Jesuit Core Honors Program, Seattle University.

He also completed the Kellogg Executive Scholar graduate certificate program in Nonprofit Management at Northwestern University.

Accomplishments, Awards, and Certifications

He is an IEEE Fellow and previous International Neural Networks Society (INNS) President, INNS Fellow, NSF CAREER Awardee, and recipient of the 2015 INNS Gabor Award, 2019 INNS Ada Lovelace Service Award, and 2023 IEEE Pioneer Award. He served as IJCNN General Chair, and on several Boards, including the St. Patrick’s School Board, Missouri S&T Newman Center Board, IEEE Neural Networks Council, INNS, and the University of Missouri Bioinformatics Consortium, chaired the Missouri S&T Information Technology and Computing Committee as well as the Student Design and Experiential Learning Center Board. He served as Interim Director of the Missouri S&T Intelligent Systems Center and as a Program Director at the National Science Foundation.

He has produced 23 Ph.D. recipients in Computer Engineering, Electrical Engineering, Systems Engineering and Computer Science.

Key research contributions are real-time design of Unsupervised and Reinforcement Learning and their applications.