Keynote Speakers
Keynote Speeches


Prof. Nikola Kasabov, IEEE Fellow and RSNZ Fellow

Auckland University of Technology, New Zealand

Nikola Kasabov is Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the INNS College of Fellows, Fellow of the NZ Institute of IT Professionals NZ, Distinguished Fellow of the Royal Academy of Engineering and the Scottish Computer Society, UK. He is the Founding Director of the KEDRI Institute and Professor of Knowledge Engineering at Auckland University of Technology (AUT), George Moore Professor of Cognitive Data Analytics of Ulster University UK and Advisory Professor of the Shanghai Jiao Tong University. Kasabov is a Past President of the Asia Pacific Neural Network Society (APNNS) and the International Neural Network Society (INNS). He is a Co-Editor-in-Chief of the Springer journal Evolving Systems and serves as Associate Editor of Neural Networks, IEEE Transactions of CDS, Information Sciences, Applied Soft Computing and other journals. His main research interests are in the areas of neural networks, computational intelligence, artificial intelligence, soft computing, bioinformatics, neuroinformatics. He has published more than 650 publications that include 10 books, 240 journal papers, 29 patents. He has presented more than 80 plenary and invited talks at international conferences. More information of Prof. Kasabov can be found on:

Speech Title: "Neuromorphic Computation for Explainable AI"

Abstract: The talk argues and demonstrates that neuromorphic (brain-inspired) computational architectures are not only capable of deep, incremental and transfer learning of temporal or spatio-temporal data, but also enabling the extraction of deep knowledge representation from the learned data and facilitating the development of open, explainable AI. The talk is focusing on the use of the third generation of artificial neural networks, the spiking neural networks (SNN), for building explainable AI. SNN use principles of learning and knowledge representation inspired by the human brain. A SNN development system NeuCube (, or publicly available, will be used as an example of how to build explainable AI systems, with applications for brain data modelling, environmental event prediction, brain-computer interfaces. The talk also presents some new directions towards knowledge transfer between humans and AI systems. More information on the topic can be obtained from: N.Kasabov, Time-space, spiking neural networks and brain inspired AI, Springer-Nature, 2018 (


Prof. David Zhang, RSC Fellow, IEEE Fellow and IAPR Fellow

Chinese University of Hong Kong (Shenzhen), China

David Zhang graduated in Computer Science from Peking University. He received his MSc in 1982 and his PhD in 1985 in both Computer Science from the Harbin Institute of Technology (HIT), respectively. From 1986 to 1988 he was a Postdoctoral Fellow at Tsinghua University and then an Associate Professor at the Academia Sinica, Beijing. In 1994 he received his second PhD in Electrical and Computer Engineering from the University of Waterloo, Ontario, Canada. He has been a Chair Professor at the Hong Kong Polytechnic University where he is the Founding Director of Biometrics Research Centre (UGC/CRC) supported by the Hong Kong SAR Government since 2005. Currently he is Presidential Chair Professor in Chinese University of Hong Kong (Shenzhen). Over past 30 years, he have been working on pattern recognition, image processing and biometrics, where many research results have been awarded and some created directions, including medical biometrics and palmprint recognition, are famous in the world. So far, he has published over 20 monographs, 500 international journal papers and 40 patents from USA/Japan/HK/China. He has been continuously listed as a Highly Cited Researchers in Engineering by Clarivate Analytics during 2014-2020. He is also ranked about 80 with H-Index 120 at Top 1,000 Scientists for international Computer Science and Electronics. Recently Professor Zhang has been selected as a Fellow of the Royal Society of Canada. He also is a Croucher Senior Research Fellow, Distinguished Speaker of the IEEE Computer Society, and an IEEE Life Fellow and an IAPR Fellow.

Speech Title: "Advanced Biometrics"

Abstract: In recent times, an increasing, worldwide effort has been devoted to the development of automatic personal identification systems that can be effective in a wide variety of security contexts. As one of the most powerful and reliable means of personal authentication, biometrics has been an area of particular interest. It has led to the extensive study of biometric technologies and the development of numerous algorithms, applications, and systems, which could be defined as Advanced Biometrics. This presentation will systematically explain this new research trend. As case studies, a new biometrics technology (palmprint recognition) and two new biometrics applications (medical biometrics and aesthetical biometrics) are introduced. Some useful achievements could be given to illustrate their effectiveness.


Prof. Yiu-ming Cheung, IEEE Fellow and IET Fellow

Hong Kong Baptist University, China

Yiu-ming Cheung (FIEEE, FIET, FBCS, FRSA, DFIETI) received Ph.D. degree from Department of Computer Science and Engineering at The Chinese University of Hong Kong. Currently, he is a full professor at Department of Computer Science in Hong Kong Baptist University. His research interests include machine learning, pattern recognition, image and video processing, and optimization. He has published over 220 articles in the high-quality conferences and journals. He is the Founding and Past Chairman of IEEE (Hong Kong) Chapter of Computational Intelligence Society, and the Chair of IEEE Computer Society Technical Committee on Intelligent Informatics. He has served as the Guest Editor / Associate Editor in several prestigious international journals, including: IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, Pattern Recognition, and Neurocomputing, to name a few. For details, please refer to:

Speech Title: "Objective-Domain Dual Decomposition: An Effective Approach to Optimizing Partially Differentiable Objective Functions"

Abstract: This paper addresses a class of optimization problems in which either part of the objective function is differentiable while the rest is nondifferentiable or the objective function is differentiable in only part of the domain. Accordingly, we propose a dual-decomposition-based approach that includes both objective decomposition and domain decomposition. In the former, the original objective function is decomposed into several relatively simple subobjectives to isolate the nondifferentiable part of the objective function, and the problem is consequently formulated as a multiobjective optimization problem (MOP). In the latter decomposition, we decompose the domain into two subdomains, that is, the differentiable and nondifferentiable domains, to isolate the nondifferentiable domain of the nondifferentiable subobjective. Subsequently, the problem can be optimized with different schemes in the different subdomains. We propose a population-based optimization algorithm, called the simulated water-stream algorithm (SWA), for solving this MOP. The SWA is inspired by the natural phenomenon of water streams moving toward a basin, which is analogous to the process of searching for the minimal solutions of an optimization problem. The proposed SWA combines the deterministic search and heuristic search in a single framework. Experiments show that the SWA yields promising results compared with its existing counterparts.