About
Mahdi Khodayar, Ph.D., received his B.Sc. degree in computer engineering and the M.Sc. degree in artificial intelligence from K.N. Toosi University of Technology, Tehran, Iran, in 2013 and 2015, respectively, and a Ph.D. degree in electrical engineering from Southern Methodist University in 2020.
He is currently an assistant professor in the department of computer science at The University of Tulsa. His main research interests include AI and machine learning. He is focused on deep learning, spatiotemporal pattern recognition, reinforcement learning, and computer vision.
Khodayar serves as an associate editor for IEEE Transactions on Transportation Electrification, IEEE Transactions on Industrial Informatics, and Elsevier’s Sustainable Computing: Informatics and Systems. He has also served as a reviewer for many reputable journals and conferences, including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Sustainable Energy, and IEEE PES General Meeting.
Khodayar’s projects are funded by the National Science Foundation (NSF), U.S. Department of Transportation (DOT), as well as the TU Cyber Fellows program.
Awards and Honors
- National Science Foundation (NSF) – Electrical, Communications and Cyber Systems (ECCS) Division – Spring 2022
- US Department of Transportation – Federal Highway Administration (FHWA) – Fall 2023
- Zelimir Schmidt Award for Early Career Research (Spring 2023), The University of Tulsa, for conducting exemplary research resulting in significant scientific advances and widespread recognition.
- Honors Student Award of Exceptional Talents (December 2015), Khajeh Nasir Toosi University of Technology, ranked 1st among all graduate students of artificial intelligence major.
Education
- Ph.D., Electrical Engineering, Southern Methodist University, 2020
- M.S., Artificial Intelligence, Khajeh Nasir Toosi University of Technology, 2015
- B.S., Software Engineering, Khajeh Nasir Toosi University of Technology, 2013
Research interests and areas of expertise
- Artificial intelligence
- Machine learning
- Deep neural networks
- Statistical pattern recognition
- Probabilistic graphical models
- Power systems
- Transportation systems
- Renewable energy
- Computer vision
- Image processing