Keynote Speakers

Prof. Ce Zhu
IEEE Fellow
University of Electronic Science and Technology of China, China

Ce Zhu received the B.S. degree from Sichuan University, Chengdu, China, in 1989, and the M. Eng and Ph.D. degrees from Southeast University, Nanjing, China, in 1992 and 1994, respectively, all in electronic and information engineering. He held a post-doctoral research position with the Chinese University of Hong Kong in 1995, the City University of Hong Kong, and the University of Melbourne, Australia, from 1996 to 1998. He was with Nanyang Technological University, Singapore, for 14 years from 1998 to 2012, where he was a Research Fellow, a Program Manager, an Assistant Professor, and then promoted to an Associate Professor in 2005. He has been with University of Electronic Science and Technology of China, Chengdu, China, as a Professor since 2012. His research interests include video coding and communications, video analysis and processing, 3D video, visual perception and applications. He has served on the editorial boards of a few journals, including as an Associate Editor of IEEE TRANSACTIONS ON IMAGE PROCESSING, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, IEEE TRANSACTIONS ON BROADCASTING, IEEE SIGNAL PROCESSING LETTERS, an Editor of IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, and an Area Editor of SIGNAL PROCESSING: IMAGE COMMUNICATION. He has also served as a Guest Editor of a few special issues in international journals, including as a Guest Editor in the IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING. He was an APSIPA Distinguished Lecturer (2021-2022), and also an IEEE Distinguished Lecturer of Circuits and Systems Society (2019-2020). He is a co-recipient of multiple paper awards at international conferences, including the most recent Best Demo Award in IEEE MMSP 2022, and the Best Paper Runner Up Award in IEEE ICME 2020.

Speech Title: TBA
Abstract: TBA

Prof. Amir Hussain
Edinburgh Napier University, UK

Amir Hussain obtained his B.Eng (1st Class Honours with distinction) and Ph.D from the University of Strathclyde in Glasgow, UK, in 1992 and 1997 respectively. Following an UK EPSRC funded Postdoctoral Fellowship (1996-98) and Research Lectureship at the University of Dundee, UK (2018-20), he joined the University of Stirling, UK, in 2000 where he was appointed to a Personal Chair in Cognitive Computing in 2012. Since 2018, he has been Director of the Centre of AI and Robotics at Edinburgh Napier University, UK. His research and innovation interests are cross-disciplinary and industry-led, aimed at developing trustworthy AI and cognitive data science technologies to engineer the smart healthcare and industrial systems of tomorrow. He has co-authored over 600 papers including around 300 journal papers (h-index: 73, 22,000+ citations) and 20 Books, and supervised over 40 PhD students. He has led major national and international projects, including as Principal Investigator of the current multi-million pound COG-MHEAR programme (funded under the UK EPSRC Transformative Healthcare Technologies for 2050 Call) that aims to develop truly personalised assistive hearing and communication technologies. He is the founding Chief Editor of (Springer's) Cognitive Computation journal and Editorial Board member for (Elsevier’s) Information Fusion and various IEEE Transactions. Amongst other distinguished roles, he is Executive Committee member of the UK Computing Research Committee (the national expert panel of the IET and BCS for UK computing research). He served as General Chair of the 2020 IEEE WCCI (the world’s largest IEEE technical event on computational intelligence, comprising the flagship IJCNN, IEEE CEC and FUZZ-IEEE) and the 2023 IEEE Smart World Congress (featuring six co-located IEEE Conferences).

Speech Title: Towards Trustworthy Artificial Intelligence: Real-world use cases, challenges and future directions
Abstract: TBA

Prof. Xinwang Liu
National University of Defense Technology, China

Xinwang Liu received his PhD degree from National University of Defense Technology (NUDT), China, in 2013. He is now Professor at School of Computer, NUDT. His current research interests include kernel learning, multi-view clustering and unsupervised feature learning. Dr. Liu has published 120+ peer-reviewed papers, including those in highly regarded journals and conferences such as IEEE T-PAMI, IEEE T-KDE, IEEE T-IP, IEEE T-NNLS, IEEE T-MM, IEEE T-IFS, ICML, NeurIPS, CVPR, ICCV, AAAI, IJCAI, etc. He is an Associate Editor of IEEE T-NNLS, IEEE TCYB and Information Fusion Journal. More information can be found at

Speech Title: SimpleMKKM: Simple Multiple Kernel K-means
We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM). It extends the widely used supervised kernel alignment criterion to multi-kernel clustering. Our criterion is given by an intractable minimization-maximization problem in the kernel coefficient and clustering partition matrix. To optimize it, we equivalently rewrite the minimization-maximization formulation as a minimization of an optimal value function, prove its differenentiablity, and design a reduced gradient descent algorithm to decrease it. Furthermore, we prove that the resultant solution of SimpleMKKM is the global optimum. We theoretically analyze the performance of SimpleMKKM in terms of its clustering generalization error. After that, we develop extensive experiments to investigate the proposed SimpleMKKM from the perspectives of clustering accuracy, advantage on the formulation and optimization, variation of the learned consensus clustering matrix with iterations, clustering performance with varied number of samples and base kernels, analysis of the learned kernel weight, the running time and the global convergence. The experimental study demonstrates the effectiveness of the proposed SimpleMKKM by considerably and consistently outperforming state of the art multiple kernel clustering alternatives. In addition, the ablation study shows that the improved clustering performance is contributed by both the novel formulation and new optimization. Our work provides a more effective approach to integrate multi-view data for clustering, and this could trigger novel research on multiple kernel clustering. The source code and data for SimpleMKKM are available at