Rate My Professor Zhenbo Wang

Zhenbo Wang is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of Tennessee, Knoxville, where he has served since August 2024, following his tenure as Assistant Professor from August 2018 to July 2024. He earned his Ph.D. from Purdue University in 2018, Master of Engineering from Beihang University in 2013, and Bachelor of Engineering from Nanjing University of Aeronautics and Astronautics in 2010. Wang directs the Autonomous Systems Laboratory, focusing on highly efficient computational methods for complex dynamical systems using novel control, optimization, and machine learning techniques for vehicle operations. His research interests include optimal control, numerical optimization, convex optimization, machine learning, guidance and control, space systems, aerial vehicles, connected and automated vehicles, and power and energy systems.

Wang has been recognized with several awards, including the Professional Promise in Research Award from the Tickle College of Engineering in 2024, the Louis and Ann Hoffman Endowed Excellence in Research Award from the Department of Mechanical, Aerospace, and Biomedical Engineering in 2023, and the NSF Faculty Early Career Development Program (CAREER) Award in 2023. He also received the Estus H. and Vashti L. Magoon Award for Excellence in Teaching and the Koerner Scholarship from Purdue University in 2018 and 2017, respectively. His key publications include "A survey on convex optimization for guidance and control of vehicular systems" (Annual Reviews in Control, 2024), "Convex approach to real-time multiphase trajectory optimization for urban air mobility" (Journal of Air Transportation, 2024), "Constrained trajectory optimization for planetary entry via sequential convex programming" (Journal of Guidance, Control, and Dynamics, 2017), "Minimum-fuel low-thrust transfers for spacecraft: A convex approach" (IEEE Transactions on Aerospace and Electronic Systems, 2018), and "Real-time optimal control for irregular asteroid landings using deep neural networks" (Acta Astronautica, 2020).