Research

My research focuses on data-driven, physics-informed, and uncertainty-aware control and optimization for electrified and intelligent transportation systems. I am particularly interested in methods that integrate measured data, partial system knowledge, and uncertainty quantification to improve reliability, efficiency, and scalability.

Data-Driven Predictive Control

I develop data-enabled and direct data-driven predictive control methods for complex dynamical systems. My work studies how data quality, informativity, noise, and computational complexity affect prediction and control performance, with applications to eco-driving and mixed traffic systems.

Battery-Aware Energy and Thermal Management

I design predictive power, energy, and thermal management strategies for electric vehicles. These methods aim to reduce energy consumption while accounting for battery degradation, thermal constraints, and connected vehicle information.

Physics-Augmented Learning and Control

I am interested in integrating partial physical knowledge with data-driven models and controllers. This includes physics-augmented data-enabled predictive control, uncertainty-aware model learning, and reliable decision-making under limited or shifted data distributions.

Electrified and Intelligent Transportation Systems

My application domains include connected and automated electric vehicles, mixed traffic flow control, EV energy management, battery thermal management, and electrified transportation systems.