About Me
I am a Postdoctoral Research Fellow in Prof. Ziyou Song’s research group at the University of Michigan, Ann Arbor. My research focuses on scientific machine learning, data-driven modeling and control, and predictive optimization for complex dynamical and energy systems.
My work aims to understand how data, partial physical knowledge, and structural relationships across dynamical systems can be jointly exploited for data-efficient, transferable, and reliable learning and decision-making. I also study data informativity, data-driven predictive control and optimization. These methodological developments are motivated by applications in electrified and intelligent transportation, including battery degradation-aware optimization, energy and thermal management, eco-driving, and connected and automated vehicles.
Before joining the University of Michigan, I received my Ph.D. in Mechanical Engineering from the National University of Singapore. I also received my B.E. and M.E. degrees in Automotive Engineering from Chongqing University.
Research Interests
- Scientific machine learning and data-driven modeling of dynamical systems
- Structure-aware and transferable learning across dynamical systems
- Physics-informed and physics-guided learning
- Data informativity and data-driven predictive control
- Battery degradation-aware optimization and energy/thermal management
- Electrified transportation and energy systems
Recent News
August 2026: Received the 2025-2026 Award for Outstanding Study Elite from the China Scholarship Council (CSC), awarded to 650 Category-A recipients worldwide annually.
June 2026: Presented our paper, “Lane Change Trajectory Planning for Personalized Driving Comfort and Mobility Efficiency,” at the IEEE Intelligent Vehicles Symposium (IV) in Detroit.
May 2026: Presented our paper, “Direct Data-driven Predictive Control: A Computationally Efficient Alternative to DeePC for Eco-driving in Mixed Traffic Flows,” at the American Control Conference.
October 2025: Led the Energy Storage Lab team to win the 1st Place Award in the IEEE VTS Motor Vehicles Challenge 2025 on “Energy Management and Control of a Marine Electric Propulsion System.” Our award-winning solution was presented at the IEEE VPPC 2025 Motor Vehicles Challenge special session. [News]