Rate My Professor Zhaojian Li

Zhaojian Li is the Red Cedar Distinguished Associate Professor in the Department of Mechanical Engineering and an Associate Professor in the Department of Electrical and Computer Engineering at Michigan State University, where he has served since August 2017, initially as an Assistant Professor. He directs the Robotics and Intelligent Vehicle Automation Lab (RIVAL). Li holds a B.E. in Civil Aviation from Nanjing University of Aeronautics and Astronautics (2010), an M.S. in Aerospace Engineering (2013), and a Ph.D. in Aerospace Engineering (2015), both from the University of Michigan, Ann Arbor. His dissertation focused on Developments in Estimation and Control for Cloud-Enabled Automotive Vehicles. Prior to academia, he worked as an Air Traffic Controller at the Shanghai Area Control Center (2010-2012), interned in the Intelligent Systems & Control group at Ford Research and Advanced Engineering (2014 and 2015), and served as a Multivariable Controls Algorithm Engineer at General Motors Engineering Propulsion Systems (2016-2017).

Li's research specializes in robotics and autonomous vehicles, intelligent transportation systems, reinforcement learning, vehicle dynamics, and optimal control. He leads funded projects including NSF CAREER: Cloud-facilitated Privacy-aware Collaborative Sensing and Control for Intelligent and Connected Vehicles (2021), a $3.5M USDA grant for an Automated and Integrated Mobile System for Apple Harvest and In-field Sorting (2023), and NSF CPS: Ensure Privacy and Truthfulness in Self-interested Multi-agent Cyber-physical Systems (2024). His innovations include robotic apple harvesting systems and privacy-preserving multi-agent control. Li has received the 2025 Outstanding Young Investigator Award from the ASME Dynamic Systems and Controls Division, 2023 ASABE Rain Bird Engineering Concept of the Year Award, 2023 ASABE ITSC Technical Community Meeting Paper Award, and 2021 NSF CAREER Award. With over 7,000 citations, key publications include "Multi-agent deep reinforcement learning for large-scale traffic signal control" (IEEE Transactions on Intelligent Transportation Systems, 2019; 1,329 citations), "Deep multi-agent reinforcement learning for highway on-ramp merging in mixed traffic" (IEEE Transactions on Intelligent Transportation Systems, 2023; 310 citations), and "YOLOWeeds: A novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems" (Computers and Electronics in Agriculture, 2023; 324 citations). He serves as Associate Editor for IEEE Transactions on Intelligent Vehicles, Evolving Systems, and International Journal of Intelligent Robotics and Applications, and has chaired sessions and organized workshops at conferences such as ACC and DSCC.