I am a second-year Ph.D. student in Mechanical Engineering at Northeastern University, advised by Prof. Sze Zheng Yong. My research lies at the intersection of control theory and machine learning, with a focus on data-driven control with constraint satisfaction, as well as safe planning and control under uncertainty.
I received my M.S. in Mechanical Engineering from the University of Illinois Urbana-Champaign (UIUC), where I was advised by Prof. Naira Hovakimyan. Prior to that, I earned my B.Eng. in Electronic and Information Engineering from The Hong Kong Polytechnic University (PolyU).
I am a second-year Ph.D. student in Mechanical Engineering at Northeastern University, advised by Prof. Sze Zheng Yong. My research lies at the intersection of control theory and machine learning, with a focus on data-driven control with constraint satisfaction, as well as safe planning and control under uncertainty.
I received my M.S. in Mechanical Engineering from the University of Illinois Urbana-Champaign (UIUC), where I was advised by Prof. Naira Hovakimyan. Prior to that, I earned my B.Eng. in Electronic and Information Engineering from The Hong Kong Polytechnic University (PolyU).
Ph.D. in Mechanical and Industrial Engineering
Northeastern University
M.S. in Mechanical Engineering
University of Illinois Urbana-Champaign
B.Eng. (Honors) in Electronic and Information Engineering
Hong Kong Polytechnic University
Safety filters constructed from control barrier functions (CBFs) are commonly appended to pre-trained neural network controllers to enforce safety requirements. However, this decoupled design with hand-tuned, fixed CBF parameters often fails to adapt to the underlying controller, …
We present a novel framework for embedding hard constraint satisfaction into neural network (NN) architectures, specifically feedforward neural networks and transformers, with input-dependent affine constraints of arbitrary cardinality. Traditional constraint enforcement …
This letter presents an output-feedback tube-based model predictive control (MPC) framework for linear sampled-data control systems subject to external disturbances and non-convex constraints. The proposed approach rigorously incorporates inter-sample reachability analysis to …
For the cascaded planning and control modules implemented for robot navigation, the frequency gap between the planner and controller has received limited attention. In this study, we introduce a novel B-spline parameterized optimization-based planner (BSPOP) designed to address …
Perception, Planning, and Control form the essential components of autonomy in advanced air mobility. This work advances the holistic integration of these components to enhance the performance and robustness of the complete cyber-physical system. We adapt Perception Simplex, a …
