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 machine learning and statistical pattern recognition. He is focused on deep learning, sparse modeling, and spatiotemporal pattern recognition.
Khodayar has served as a Reviewer for many reputable journals, including the IEEE Transactions on Neural Networks and Learning Systems, the IEEE Transactions on Industrial Informatics, the IEEE Transactions on Fuzzy Systems, the IEEE Transactions on Sustainable Energy, and the IEEE Transactions on Power Systems.
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