Maged Shoman is a research assistant professor in Intelligent Transportation Systems with the University of Tennessee-Oak Ridge Innovation Institute’s Energy Storage and Transportation Convergent Research Initiative. 

Shoman’s research interests are Deep Learning, Computer Vision, Transportation and Traffic Safety Research, Autonomous and Connected Vehicles, Digital Twins and Smart Cities and Intelligent Transportation Systems (ITS). His research has been supported by various funding agencies, including the National Science Foundation (NSF), the U.S. Department of Transportation (DOT), as well as state DOTs and municipalities. He has published over 10 peer-reviewed papers in prestigious journals and conferences, including IEEE Computer Vision and Pattern Recognition (CVPR), ASCE Journal of Transportation Engineering, and the Transportation Research Record. His research has been featured on St Louis NPR, and he has received multiple accolades, including placements in the NVIDIA AI City Challenge and the 2021 Outstanding Ph.D. Student Award. 

Shoman’s dissertation “Interactive, Multi-Purpose Traffic Prediction Platform Using Connected Vehicles Data,” serves as a platform for transportation planners to understand data through visual analysis, predict the future of traffic flow and speech query bottlenecks. At the Technical University of Munich his master’s thesis explored the impact of Transportation Network Companies (TNC) services on urban mobility. 

Shoman has been actively involved in professional organizations, serving in leadership roles in the Institute of Transportation Engineers (ITE) and the American Society of Civil Engineers (ASCE). He is also a dedicated reviewer for top journals, including IEEE CVPR, ASCE Journal of Transportation Engineering, Part A: Systems, Transportation Research Record and IEEE International Conference on Intelligent Transportation Systems (ITSC).

Education