Ruigang (Ray) Wang

Postdoctoral Researcher, ruigang.wang@sydney.edu.au

Australian Centre for Robotics (ACFR) | The University of Sydney

My research focuses on fundamental connections between control theory and machine learning:

1. Control theoretic tools for certified robustness of deep neural networks;
2. Trustworthy ML for complex system modeling, estimation, control and decision making;
3. Safety-critical applications such as human-robot interaction.

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.

News

15/10/25

Our paper "LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers" has been provisionally accepted by Automatica.

28/07/25

Our paper "Robustly Invertible Nonlinear Dynamics and the BiLipREN: Contracting Neural Models with Contracting Inverses" has been accepted by CDC 2025.

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).

03/03/25

I joined the Reliable and Secure AI team at ServiceNow Research as a visiting researcher.

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