Prof. Benjamin W. Wah, IEEE/ACM/AAAS Fellow
The Chinese University of Hong Kong, Hong Kong

Benjamin W. Wah is currently the Provost and Wei Lun Professor of Computer Science and Engineering of the Chinese University of Hong Kong, as well as the Chair of the Research Grants Council of the University Grants Committee, Hong Kong, and the Franklin W. Woeltge Emeritus Professor of Electrical and Computer Engineering, University of Illinois, Urbana-Champaign. Before then, he served as the Director of the Advanced Digital Sciences Center in Singapore, as well as the Franklin W. Woeltge Professor of Electrical and Computer Engineering and Professor of the Coordinated Science Laboratory of the University of Illinois, Urbana-Champaign, IL. Wah received his Ph.D. degree in computer science from the University of California, Berkeley, CA, in 1979. He has received numerous awards for his contributions, which include the IEEE CS Technical Achievement Award (1998), the IEEE Millennium Medal (2000), the IEEE-CS W. Wallace-McDowell Award (2006), the Pan Wen-Yuan Outstanding Research Award (2006), the IEEE-CS Richard E. Merwin Award (2007), the IEEE-CS Tsutomu Kanai Award (2009), the Distinguished Alumni Award in Computer Science of the University of California, Berkeley (2011), and the Justice of Peace, Hong Kong (2018). Wah's current research interests are in the areas of big data applications and multimedia design and processing
Wah cofounded the IEEE Transactions on Knowledge and Data Engineering in 1988 and served as its Editor-in-Chief between 1993 and 1996. He currently serves as the Honorary Editor-in-Chief of Knowledge and Information Systems and is on the editorial boards of Information Sciences, International Journal on Artificial Intelligence Tools, Journal of VLSI Signal Processing, World Wide Web, and Journal of Computer Science and Technology. Wah has served the IEEE Computer Society in various capacities, including Vice President for Publications (1998 and 1999) and President (2001). He is a Fellow of the AAAS, ACM, and IEEE.

Title--Using Dominance to Harness the Complexity of Big Data Applications
Abstract--Big Data is emerging as one of the hottest multi-disciplinary research fields in recent years. Big data innovations are transforming science, engineering, medicine, healthcare, education, finance, business, and ultimately society itself. However, as their data space is so vast that it is infeasible to scan the data once, we must focus our search on promising subspaces. We introduce the concept of kernels that represent solution density in a subspace. To avoid scanning through the entire data, we prune inferior subspaces with a small kernel using some dominance relations between subspaces P_i and P_j . In this case, when P_i dominates P_j we can prune P_j  because we can guarantee that the kernel in P_j cannot be better than that in P_i , without search both subspaces. This approach is significantly more effective than heuristic pruning which does not provide such guarantees. For illustration, we present the learning and generalization of methods for identifying subspaces with high daily returns in stock trading, and the identification of regions with high perceptual quality in interactive multimedia.
 

Prof. Ying Tan
Peking University, China

Ying Tan is a professor of Peking University, and director of Computational Intelligence Laboratory at Peking University. He worked at Faculty of Design, Kyushu University, Japan, as a professor, and at Columbia University as senior research fellow and at Chinese University of Hong Kong in 1999 and 2004-2005 as a research associate/fellow, and at University of Science and Technology of China in 1998, 2005-2006 as a professor under the 100-talent program of CAS, etc. He is the inventor of Fireworks Algorithm (FWA). He serves as the Editor-in-Chief of International Journal of Computational Intelligence and Pattern Recognition (IJCIPR), the Associate Editor of IEEE Transactions on Evolutionary Computation (TEC), IEEE Transactions on Cybernetics (CYB), International Journal of Swarm Intelligence Research (IJSIR), International Journal of Artificial Intelligence (IJAI), etc. He also served as an Editor of Springer’s Lecture Notes on Computer Science (LNCS) for 32+ volumes, and Guest Editors of several referred Journals, including IEEE/ACM Transactions on Computational Biology and Bioinformatics, Information Science, Neurocomputing, Natural Computing, Swarm and Evolutionary Optimization, etc. He is a senior member of IEEE. He is the founder general chair of the ICSI International Conference series since 2010 and the DMBD conference series since 2016. He won the 2nd-Class Natural Science Award of China in 2009 and many best paper awards. His research interests include computational intelligence, swarm intelligence, swarm robotics, data mining, machine learning, intelligent information processing for information security and financial prediction, etc. He has published more than 300+ papers in refereed journals and conferences in these areas, and authored/co-authored 12 books, including “Fireworks Algorithm” by Springer-Nature in 2015, and “GPU-based Parallel Implementation of Swarm Intelligence Algorithms” by Morgan Kaufmann (Elsevier) in 2016, and 28 chapters in book, and received 4 invention patents.

Title--Novel Swarm Intelligence Algorithms-Fireworks Algorithm and Its Applications
Abstract--Inspired from the collective behaviors of many swarm-based creatures in nature or social phenomena, swarm intelligence (SI) has been received attention and studied extensively, gradually becomes a class of efficiently intelligent optimization methods. Inspired by fireworks’ explosion in air, the so-called fireworks algorithm (FWA) was proposed in 2010. Since then, many improvements and beyond were proposed to increase the efficiency of FWA dramatically, furthermore, a variety of successful applications were reported to enrich the studies of FWA considerably. In this talk, the novel swarm intelligence algorithm, i.e., fireworks algorithm, is briefly introduced and reviewed, then several effective improved algorithms are highlighted, individually. In addition, the multi-objective fireworks algorithm and the graphic processing unit (GPU) based FWA are also briefly presented, particularly the GPU-based FWA is able to speed up the optimization process extremely. Extensive experiments on benchmark functions demonstrate that the improved algorithms significantly increase the accuracy of found solutions, yet decrease the running time sharply. Finally, several typical applications of FWA are presented in detail.
 

Prof. Ben Niu
Shenzhen University, China

Dr. Ben Niu is a Zhujiang Scholarship Professor and Head with Department of Management Science, Shenzhen University, China. Dr Niu had experiences of visiting at University of Edinburgh in 2018, Arizona State University in 2016, Victoria University of Wellington in 2013, and The Hong Kong University in 2011. During 2013 to 2016 he had been working as a Postdoctoral Research Fellow at The Hong Kong Polytechnic University. In the recent 5 years, he was awarded several honors, including ‘ Hong Kong Scholar’ , ‘Zhujiang Scholar of Guangdong Province’, ’Thousand Hundred Ten Talents of Guangdong Province’, Top Youth Talent of Guangdong Province‘, ‘Overseas High-Caliber Personnel of Shenzhen’, and ‘High-Level Professional in Shenzhen’. As a principle investigator, he supervised 5 projects from National Natural Science Foundation of China, 2 projects from Chinese Postdoctoral Science Foundation. He has published more than 150 papers in the international Journals and international conferences, among which 40 are in refereed international journals, including IEEE/ACM Transactions, AMC, COR, NC, IJPR, IJIM,CIE et al. His main fields of research are Artificial Intelligent Optimization, Operation Research and their applications on Big Data Analysis, Financial Engineering, Business Intelligence, Supply Chain Optimization, and Resource Optimization.

Title--Recent Advances in Bacteria-inspired Optimization Algorithms
Abstract--Nowadays, most swarm intelligent optimization algorithms are inspired by the behavior of animals. By simulating the foraging behavior of E. coli in human intestines, Bacterial Foraging Algorithm (BFO) was proposed. However, due to lacking of communication among individuals and adopting nested loop, original BFO has several defects, including high memory consumption and low efficiency. Therefore, two BFO variants, including Bacterial Colony Optimization (BCO) and Coevolutionary Structure-Redesigned-Based Bacterial Foraging Optimization (CSRBFO), were developed to address the problems above. To be more specific, BCO considers chemotaxis strategy combined with communication mechanism, while CSRBFO aims to fulfill tasks more effectively by using a single loop to replace the nested loop of BFO. In this talk, initially, original BFO is described reviewed and its two variants (BCO and CSRBFO) are highlighted respectively. Then, some effective strategies for other improved Bacteria-inspired Optimization algorithms are introduced in detail. Finally, some real-world applications and future work of Bacteria-inspired Optimization Algorithms are discussed.