About Me

I received the B.E. in Automobile Engineering from Beihang University in 2009, and received the M.E. in Mechatronics Engineering from Shanghai Jiao Tong University in 2012. During 03/2012 - 07/2013, I worked as a high-precision motion control engineer in Shanghai Micro Electronics Equipment Co., China. I received the Ph.D. in Chemical Engineering (Process Control) from the University of New South Wales (UNSW) in 2017, supervised by Prof. Jie Bao. Between 04/2017 and 09/2018, I was a postdoctoral researcher in the School of Chemical Engineering at UNSW. Since then, I work as a postdoctoral researcher in ACFR at the University of Sydney, advised by Prof. Ian Manchester. During 03/2025 - 07/2025, I was a visiting scholar at at ServiceNow Research.

News

23/01/26
Our paper "LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers" has been accepted as a regular paper by Automatica.
03/04/25
Our paper "R2DN: Scalable Parameterization of Contracting and Lipschitz Recurrent Deep Networks" has been posted on arXiv (joint work with Nicholas H. Barbara and Ian R. Manchester).
31/01/25
Our paper "Norm-Bounded Low-Rank Adaptation" has been posted on arXiv. TL;DR: Complete parameterization of low-rank adpation for LLM with bounded rank and norm

Selected Publications

Google Scholar
LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers
Automatica, regular paper, 2026
Patricia Pauli, Ruigang Wang, Ian R. Manchester, Frank Allgöwer

Monotone, Bi-Lipschitz, and Polyak-Lojasiewicz Networks
International Conference on Machine Learning (ICML), 2024
Ruigang Wang, Krishnamurthy Dvijotham, Ian R. Manchester

Recurrent Equilibrium Networks: Flexible Dynamic Models with Guaranteed Stability and Robustness
IEEE Transactions on Automatic Control (TAC, full paper), 2024
Max Revay, Ruigang Wang, Ian R. Manchester

Direct parameterization of lipschitz-bounded deep networks
International Conference on Machine Learning (ICML, Oral), 2023
Ruigang Wang, Ian R. Manchester

Reduced-order nonlinear observers via contraction analysis and convex optimization
IEEE Transactions on Automatic Control (TAC, full paper), 2022
Bowen Yi, Ruigang Wang, Ian R. Manchester

On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks
Systems Theory in Data and Optimization: Proceedings of SysDO 2024
Nicholas H. Barbara, Ruigang Wang, Ian R. Manchester

Learning stable and passive neural differential equations
63nd IEEE Conference on Decision and Control (CDC), 2024
Jing Cheng, Ruigang Wang, Ian R. Manchester

Learning Over All Stabilizing Nonlinear Controllers for a Partially-Observed Linear System
IEEE Control Systems Letters, 2023
Ruigang Wang, Nicholas H. Barbara, Max Revay, Ian R. Manchester

Learning stable and robust linear parameter-varying state-space models
62nd IEEE Conference on Decision and Control (CDC), 2023
Chris Verhoek, Ruigang Wang, Roland Toth

Learning Over Contracting and Lipschitz Closed-Loops for Partially-Observed Nonlinear Systems
62nd IEEE Conference on Decision and Control (CDC), 2023
Nicholas H. Barbara, Ruigang Wang, Ian R. Manchester