معرفی
Mahdi Khodayar is an Assistant Professor of Computer Science at the University of Tulsa. He holds a Ph.D. in Electrical Engineering from Southern Methodist University (2020) and M.Sc./B.S. degrees in Artificial Intelligence and Software Engineering from K.N. Toosi University of Technology (2015, 2013). His research focuses on AI and machine learning applications in power systems, transportation systems, computer vision, and spatiotemporal pattern recognition.
- B.Sc. in Computer Engineering (2013), K.N. Toosi University of Technology
- M.Sc. in Artificial Intelligence (2015), K.N. Toosi University of Technology
- Ph.D. in Electrical Engineering (2020), Southern Methodist University
Khodayar’s research bridges deep learning with energy systems, including fault detection in power grids, renewable energy forecasting, and traffic scene understanding. His work leverages graph neural networks, reinforcement learning, and generative models for robust spatiotemporal analysis.
Recent publications highlight his focus on graph-based architectures for power systems, hybrid deep reinforcement learning in energy forecasting, and unsupervised domain adaptation in remote sensing. His work integrates physics-informed modeling with probabilistic frameworks.
Scientific Awards:
- NSF ECCS Division Funding (2022)
- US DOT FHWA Grant (2023)
- Zelimir Schmidt Award for Early Career Research (2023)
- Honors Student Award (2015), K.N. Toosi University
Khodayar has been funded by the NSF, US Department of Transportation, and the TU Cyber Fellows program. He serves as an associate editor for IEEE Transactions on Transportation Electrification and other journals.
