• Office Address: 315 Min H. Kao Building
• Phone: 865-974-5902
• E-mail: dongshed@utk.edu

Education

• PhD in Electrical Engineering, University of Southern California, 2022
• MS in Electrical Engineering, University of Minnesota, Twin Cities, 2017
• BE in Automation and ME in Control Theory and Engineering, Zhejiang University, 2011 and 2015

Dongsheng Ding is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville (UT). He received his PhD in electrical engineering from the University of Southern California in 2022 and was a Postdoctoral Researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania from September 2022 to July 2025. His research applies principles and tools from optimization and control to the study of requirement-driven machine decision-making. He develops principled methodologies that enable reinforcement learning and generative modeling to operate under specific requirements, such as safety, fairness, and other operational constraints. His research interests include constrained reinforcement learning, multi-agent reinforcement learning, constrained generative modeling, alignment and composition of diffusion and large language models, and robustness and resilience analysis of optimization algorithms. He has been recognized as an MHI PhD Scholar Finalist (2018, 2021) at USC and an Expert Reviewer for ICML (2021).

• Optimization and control
• Reinforcement learning
• Generative artificial intelligence