PLEASE JOIN OUR FRIDAY SEMINARS

The CCCN-CityU-PolyU Joint Seminar Series began in 2001, and has since become regular weekly meetings for visitors, faculty, researchers and students to discuss latest progresses in their research.

The next seminar is:

Date & Time: September 11, 2026, Friday, 3:00pm-4:00pm
Title: Learning to Evolve: Machine Learning for Complex Dynamic Systems

Speaker: Dr Tiexin Qin, City University of Hong Kong

Venue: FYW-3316 CityU
Zoom ID 859 8869 4437
Password 123456

Abstract:
Our physical world is inherently dynamic, governed by continuous change across spatial and temporal scales. Understanding and modeling such complex dynamics stand as a cornerstone in the realm of modern science and engineering. Standing at the confluence of artificial intelligence and physics, I have always been driven by a central question: how can we build intelligent systems capable of interpreting and adapting to our changing world in a human-like manner? Despite notable progress in AI, this problem remains fundamentally challenging due to nonstationary data distributions, nonlinear and often stochastic dynamics, and temporal as well as multiscale interactions under partial observability in real-world systems. To this end, my research focuses on developing innovative machine learning algorithms that can effectively model and reason about temporal interactions in complex dynamical systems through an integration of AI techniques, mathematical tools, physical principles, alongside exploration of real-world applications, to enhance the adaptability, interpretability, and robustness of machine learning architectures when applied to time-varying and structure-rich data. In this presentation, I will talk about the key contributions of my prior research, which is centered on advancing both the theoretical underpinnings and practical capabilities of AI in complex, dynamic environments.

Speaker's Bio:
Tiexin Qin is currently a postdoctoral researcher at the City University of Hong Kong. He received his Ph.D. degree with Department of Electrical Engineering, City University of Hong Kong, Hong Kong, in 2025. Before that, he obtained the M.S. degree in computer science from Nanjing University, Jiangsu, China, in 2021 and the B.S. degree in electronic information science and technology from China University of Mining and Technology, Jiangsu, China, in 2018. His research interests encompass machine learning for complex systems, neural differential equations, and transfer learning, with a particular focus on their grounded mathematical principles and practical applications.

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Our Mission

The Centre for Complexity and Complex Networks aims to conduct emerging and cutting-edge research in the multidisciplinary area of complex systems and networks, including fundamental theory in dynamical networked systems and cyber physical systems, and applications in

epidemic progression modelling
energy systems and power grids
information and communication systems
transportation networks
resilience of critical infrastructures
cryptocurreny and blockchains
business and finance

Through the significant and groundbreaking contributions of its members to the fundamental theory of nonlinear science and applications over the past 20 more years, the centre has established itself as one of the leading research centres in the world focusing on nonlinear science, complexity and complex systems.

Our centre promotes inter-institutional and interdisciplinary collaborations, and supports the industrial and business development of Hong Kong and the mainland via technology transfer and joint research projects.

WHAT'S NEWS

New Journal

At the invitation of World Scientific, Prof. Michael Tse will become the Founding Editor-in-Chief of the Journal of Digital Energy Conversion, which is dedicated to advancing the research and applications of modern power conversion and energy systems.

Congratulations to Prof. Michael Tse on his being inducted to the Hong Kong Academy of Engineering as its Fellow.

The Hong Kong Academy of Engineering announced on November 26, 2025, the induction of twelve exceptional professionals as Fellows, who were elected at its 32nd annual general meeting on September 8, 2025 and were formally inducted at a ceremony held on November 24, 2025 at HOPEWELL Hotel. The citation for Prof. Tse's election is "For pioneering contributions in power electronics, including ultra-fast-transient voltage regulation, sliding-mode control for converters, robust EV charging, and wireless power transfer."

Congratulations to Prof. Ron Chen on being awarded The Ichtiaroglou Award
The Ichtiaroglou Award recognizes Prof. Chen's lifetime achievement in Nonlinear Systems Theory. The award ceremony will be held at the Second DANOC Days of Applied Nonlinearity and Complexity in January 2026.

Our Centre and The Applied Mathematical Science Research Centre of Jiangsu Province have recently signed an MoU to promote academic research, exchange, and sharing of results and resource, aiming to promote high-quality research and knowledge transfer in areas of complex systems, networks, applied mathematics. This agreement will strengthen the collaborative relationship between the two centres, and foster talent exchange and development between Hong Kong and Jiangsu Province.

Congratulations!

Prof Ron Chen was elected as NetSci Fellow 2025! The NetSci Fellowship recognizes researchers who have made outstanding and significant contributions to network science research and to the community of network scientists. Elected nominees are named Fellows of the Network Science Society. The Fellowship will be awarded each year to up to 7 members of the community on the basis of their exceptional life-long individual contributions to any area of network science research and to the community of network scientists (locally and globally). All members of the community are eligible for nomination and election to the Fellowship. Selection from the pool of nominees will be conducted by the Fellowship Committee, to be appointed by the Network Science Society board and composed of a minimum of 4 prominent researchers active in the area of network science.

Centre for Complexity and Complex Networks

The world is complex and connected...

Complex emerging power infrastructure

PLEASE JOIN OUR FRIDAY SEMINARS

Our Mission

Date & Time:	September 11, 2026, Friday, 3:00pm-4:00pm
Title:	Learning to Evolve: Machine Learning for Complex Dynamic Systems
Speaker:	Dr Tiexin Qin, City University of Hong Kong
Venue:	FYW-3316 CityU Zoom ID 859 8869 4437 Password 123456
Abstract:	Our physical world is inherently dynamic, governed by continuous change across spatial and temporal scales. Understanding and modeling such complex dynamics stand as a cornerstone in the realm of modern science and engineering. Standing at the confluence of artificial intelligence and physics, I have always been driven by a central question: how can we build intelligent systems capable of interpreting and adapting to our changing world in a human-like manner? Despite notable progress in AI, this problem remains fundamentally challenging due to nonstationary data distributions, nonlinear and often stochastic dynamics, and temporal as well as multiscale interactions under partial observability in real-world systems. To this end, my research focuses on developing innovative machine learning algorithms that can effectively model and reason about temporal interactions in complex dynamical systems through an integration of AI techniques, mathematical tools, physical principles, alongside exploration of real-world applications, to enhance the adaptability, interpretability, and robustness of machine learning architectures when applied to time-varying and structure-rich data. In this presentation, I will talk about the key contributions of my prior research, which is centered on advancing both the theoretical underpinnings and practical capabilities of AI in complex, dynamic environments.
Speaker's Bio:	Tiexin Qin is currently a postdoctoral researcher at the City University of Hong Kong. He received his Ph.D. degree with Department of Electrical Engineering, City University of Hong Kong, Hong Kong, in 2025. Before that, he obtained the M.S. degree in computer science from Nanjing University, Jiangsu, China, in 2021 and the B.S. degree in electronic information science and technology from China University of Mining and Technology, Jiangsu, China, in 2018. His research interests encompass machine learning for complex systems, neural differential equations, and transfer learning, with a particular focus on their grounded mathematical principles and practical applications.