Prof. Jun Wang
City University of Hong Kong, Hong Kong
Biography: Jun Wang is the Chair Professor Computational Intelligence in the Department of Computer Science at City University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, University of North Dakota, and the Chinese University of Hong Kong. He also held various short-term visiting positions at USAF Armstrong Laboratory, RIKEN Brain Science Institute, Dalian University of Technology, Huazhong University of Science and Technology, and Shanghai Jiao Tong University (Changjiang Chair Professor). He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology and his Ph.D. degree in systems engineering from Case Western Reserve University. His current research interests include neural networks and their applications. He published about 200 journal papers, 15 book chapters, 11 edited books, and numerous conference papers in these areas. He is the Editor-in-Chief of the IEEE Transactions on Cybernetics. He also served as an Associate Editor of the IEEE Transactions on Neural Networks (1999-2009), IEEE Transactions on Cybernetics and its predecessor (2003-2013), and IEEE Transactions on Systems, Man, and Cybernetics – Part C (2002–2005), as a member of the editorial board of Neural Networks (2012-2014), editorial advisory board of International Journal of Neural Systems (2006-2013. He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence, and a Program Chair of the IEEE International Conference on Systems, Man, and Cybernetics (2012). He has been an IEEE Computational Intelligence Society Distinguished Lecturer (2010-2012, 2014-2016). In addition, he served as President of Asia Pacific Neural Network Assembly (APNNA) in 2006 and many organizations such as IEEE Fellow Committee; IEEE Computational Intelligence Society Awards Committee; IEEE Systems, Man, and Cybernetics Society Board of Governors, He is an IEEE Fellow, IAPR Fellow, and a recipient of an IEEE Transactions on Neural Networks Outstanding Paper Award and APNNA Outstanding Achievement Award in 2011, Neural Networks Pioneer Award from IEEE Computational Intelligence Society (2014), among others.
Title of Speech: Collaborative Neurodynamic Optimization: Biologically and Socially Plausible Approaches to Constrained Optimization
Abstract: The past three decades witnessed the birth and growth of neurodynamic optimization which has emerged and matured as a powerful approach to real-time optimization due to its inherent nature of parallel and distributed information processing and the hardware realizability. Despite the success, almost all existing neurodynamic approaches work well only for convex and generalized-convex optimization problems with unimodal objective functions. Effective neurodynamic approach to constrained global optimization with multimodal objective functions is rarely available. In this talk, starting with the idea and motivation of neurodynamic optimization, I will review the historic review and present the state of the art of neurodynamic optimization with many individual models for convex and generalized convex optimization. In addition, I will present a multiple-time-scale neurodynamic approach to selected constrained optimization. Finally, I will introduce population-based collaborative neurodynamic approaches to constrained distributed and global optimization. By deploying a population of individual neurodynamic models with diversified initial states at a lower level coordinated by using some global search and information exchange rules (such as PSO or DE) at a upper level, it will be shown that many constrained global optimization problems could be solved effectively and efficiently.
Prof. Amir Hussain
Head, Data Science and Security Research Group
Director, Cognitive Big Data Informatics (CogBID) Research Lab
Programme Director: Big Data Science Research Masters, and Professional Doctorate
Division of Computing Science and Maths, School of Natural Science
University of Stirling, UK
Biography: Amir Hussain is full Professor of Computing Science at the University of Stirling in Scotland. He obtained his BEng in Electronic and Electrical Engineering (with the highest 1st Class Honours, with distinction) and PhD (in novel neural network architectures and algorithms for real-world applications), both from the University of Strathclyde in Glasgow, UK, in 1992 and 1997 respectively. Following a Research Fellowship at the University of Paisley, UK (1996-98), and a Research Lectureship at the University of Dundee, in Scotland, UK (1998-2000), he joined the University of Stirling in Scotland, in 2000, where he is currently full Professor and founding Director of the Cognitive Big Data Informatics (CogBID) Laboratory, and Head of the Data Science and Security Research Group. He is founding Editor-in-Chief of Springer Nature’s internationally leading Cognitive Computation journal (current ISI SCI Impact Factor: 1.93) and the new Big Data Analytics journal (published by BioMed Central (BMC), part of Springer Nature). He is founding Editor-in-Chief for two (Springer) Book Series: Socio-Affective Computing and Cognitive Computation Trends, and also serves on the Editorial Board of a number of other world-leading journals including, as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man and Cybernetics (Systems) and the IEEE Computational Intelligence Magazine.
Professor Hussain’s research interests are cross-disciplinary and industry focused, aimed at pioneering next-generation brain-inspired multi-modal Big Data cognitive technology for solving complex real world problems, including cognitive multi-modal hearing systems, sentic computing, multilingual sentiment and opinion mining and natural language processing, medical and social multi-media analytics and gamification, manifold-based learning, dimensionality reduction and visualization, personalized and preventative (e and m) healthcare, agent based complex autonomous systems modeling and control, more natural multi-modal human computer interaction, assistive technology & related clinical research. His pioneering ongoing research on next-generation multi-modal cognitive hearing systems/assistive technology, and cognitive autonomous systems control, has received significant national and international media coverage (e.g. recently in the BBC: http://www.bbc.com/news/uk-scotland-tayside-central-33098322) In 2016, Professor Hussain was ranked, in an independent survey published in Elsevier’s leading Information Processing and Management Journal, as one of the world’s top two most productive, highly cited researchers in the sentiment analytics field (since 2000). His pioneering research on Sentic Computing for Big Data Analytics, was awarded the top “Outstanding” (industrial) Impact evaluation by the UK Government’s Research Excellence Framework (REF2014) Exercise. He has (co)authored more than 300 publications (with more than 100 journal papers, and a dozen Books, including the world’s first research monographs in the multi-disciplinary areas of: cognitively-inspired audio-visual speech filtering, sentic computing, and cognitive agent based computing). He has led major multi-disciplinary research projects (worth over $3million), as Principal Investigator, funded by national and European research councils, local and international charities and industry. He has supervised more than 30 PhDs to-date, and serves as an International Advisor and Consultant to various Governmental Higher Education and Research Councils, Universities and Companies. He regularly acts as invited Keynote Speaker, and has organized (as General/Organizing co-Chair) over 100 leading international Conferences to-date. He is an invited member of several IEEE Technical Committees (TCs), including Vice-Chair of the Emergent Technologies Technical Committee of the IEEE Computational Intelligence Society. He is Chapter Chair of the IEEE UK & RI Industry Applications Society Chapter. He is a Fellow of the UK Higher Education Academy (HEA), and Senior Fellow of the Brain Sciences Foundation (USA).
Prof. Yew-Soon Ong
Nanyang Technological University, Singapore
Biography: Yew-Soon Ong is Professor and Chair of the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He is Director of the Data Science and Artificial Intelligence Research Center, Director of the A*Star SIMTECH-NTU Joint Lab on Complex Systems and Principal Investigator of the Data Analytics & Complex System Programme in the Rolls-Royce@NTU Corporate Lab. He received his PhD from University of Southampton, UK.
Dr. Ong is founding Editor-In-Chief of the IEEE Transactions on Emerging Topics in Computational Intelligence, founding Technical Editor-In-Chief of Memetic Computing Journal (Springer), Associate Editor of IEEE Transactions on Evolutionary Computation, IEEE Transactions on Neural Network & Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Big Data, and many others. His research interests in computational intelligence span across memetic computation, evolutionary optimization using approximation/surrogate/meta-models, complex design optimization, intelligent agents and Big Data Analytics. His research grant comprises of external funding from both national and international partners that exceed 15 Million USD. Dr. Ong’s research has advanced the academic standing of evolutionary computation, earning him the recognition of a Thomson Reuters Highly Cited Researcher for two consecutive years (2015 and 2016) and a position among the World's Most-Influential-Scientific Minds. He received the 2015 IEEE Computational Intelligence Magazine Outstanding Paper Award and the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award for his work pertaining to Memetic Computation.