About me

I am a Master’s graduate in Electrical Engineering from the University of Pennsylvania, with a research focus on learning-augmented optimization and control for autonomous robotic systems under hard safety and real-time constraints. My work lies at the intersection of model predictive control, mixed-integer optimization, and data-driven dynamics learning, with the goal of bridging classical model-based methods and modern learning approaches in safety-critical settings.

I received my undergraduate degree in Electrical Engineering through a dual-degree program between Zhejiang University and the University of Illinois at Urbana–Champaign, where I built a strong foundation in system dynamics, feedback control, and embedded systems. This training was complemented by extensive hands-on experience with sensors, embedded platforms, and robotic prototypes, shaping my interest in deploying theoretically grounded control algorithms on real hardware.

At Penn, my research has increasingly focused on learning-to-optimize frameworks for control and decision-making, including model predictive control with learned components, data-driven dynamics modeling, and safe autonomy under uncertainty. I am particularly interested in methods that combine learning with explicit optimization structure to achieve reliability, interpretability, and predictable runtime in real-world robotic systems.