Speaker : Prof. C. L. Philip Chen
Abstract : Deep learning is a machine learning algorithm that attempt to learn in multiple levels, corresponding to different levels of abstraction. It is typically used to abstract useful information from data. The levels in these learned statistical models correspond to distinct levels of concepts, where higher-level concepts are defined from lower-level ones, and the same lower level concepts can help to define many higher-level concepts. Alternatively, the main advantage of deep learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of existing deep networks are based on or related to it. This talk is to overview the foundation, data representation capability of deep networks, and efficient deep learning algorithms, a newly designed fuzzy-RBM, and meaningful applications.
Biography : C. L. Philip Chen (S’88–M’88–SM’94–F’07) received the M.S. degree in electrical engineering from the University of Michigan, Ann Arbor, in 1985 and the Ph.D. degree in electrical engineering from Purdue University, West Lafayette, IN, in 1988. After having worked at U.S. for 23 years as a tenured professor, as a department head and associate dean in two different universities, he is currently the Dean of the Faculty of Science and Technology, University of Macau, Macau, China and a Chair Professor of the Department of Computer and Information Science. Dr. Chen is a Fellow of the IEEE and AAAS. He has been the President of IEEE Systems, Man, and Cybernetics Society (2012-2013). Currently, he is the Editor-in-Chief of IEEE Transactions on Systems, Man, and Cybernetics: Systems (2014-) and an associate editor of IEEE Access, IEEE/CAA Automatica Sinica, and several IEEE Transactions. He is also the Chair of TC 9.1 Economic and Business Systems of IFAC. His research areas are systems, cybernetics, and computational intelligence. He is also Chinese Association of Automation Executive board member, Chinese Association of Automation Fellow, Hong Kong Institution of Engineers Fellow. In addition, he is an ABET (Accreditation Board of Engineering and Technology Education) Program Evaluator for Computer Engineering, Electrical Engineering, and Software Engineering programs.
Speaker : Prof. Chin-Teng Lin
Abstract : Brain-Computer Interface (BCI) enhances the capability of a human brain in communicating and interacting with the environment directly. BCI plays an important role in natural cognition, which concerns the studies of brain and behavior at work for enhancing or restoring cognitive functions. Many people may benefit from BCI, which facilitates continuous monitoring of fluctuations in cognitive states under monotonous conditions in workplace or at home. People who suffer from episodic or progressive cognitive impairments in daily life can also benefit from BCI. In this talk, I will first introduce the current status of BCI and its major obstacles: lack of wearable EEG devices, various forms of noise contamination, user/circadian variability, and lack of suitable adaptive cognitive modeling. I will then introduce some methodologies to overcome these obstacles, including discovering the fundamental physiological changes of human cognitive functions at work and then utilizing these main bio-findings and computational intelligence (CI) techniques to monitor, maintain, or track human cognitive states and operating performance. In the second part of my presentation, I will introduce an innovative BCI-inspired research domain called Cyber-Brain-Physical Systems. Some future research directions in this domain will be explored and discussed, including BCI embedded wearable computing, BCI-based neuro-prosthesis and assistive devices, wearable cognitive robots, and BCI-empowered training. The potential real-life applications of BCI on various aspects of training/education, healthcare, rehabilitation, and medical treatment will also be introduced and discussed.
Biography : Dr. Chin-Teng Lin received the B.S. degree from National Chiao-Tung University (NCTU), Taiwan in 1986, and the Master and Ph.D. degree in electrical engineering from Purdue University, USA in 1989 and 1992, respectively. He is currently the Chair Professor of Electrical and Computer Engineering, Director of Brain Research Center, National Chiao Tung University, International Faculty of University of California at San-Diego (UCSD), Adjunct Professor of University of Technology Sydney, and Honorary Professorship of University of Nottingham. Dr. Lin was elevated to be an IEEE Fellow for his contributions to biologically inspired information systems in 2005, and was elevated International Fuzzy Systems Association (IFSA) Fellow in 2012. He is elected as the Editor-in-chief of IEEE Transactions on Fuzzy Systems since 2011. He also served on the Board of Governors at IEEE Circuits and Systems (CAS) Society in 2005-2008, IEEE Systems, Man, Cybernetics (SMC) Society in 2003-2005, IEEE Computational Intelligence Society in 2008-2010, and Chair of IEEE Taipei Section in 2009-2010. Dr. Lin was the Distinguished Lecturer of IEEE CAS Society from 2003 to 2005. He served as the Deputy Editor-in-Chief of IEEE Transactions on Circuits and Systems-II in 2006-2008. Dr. Lin was the Program Chair of IEEE International Conference on Systems, Man, and Cybernetics in 2005 and General Chair of 2011 IEEE International Conference on Fuzzy Systems. Dr. Lin is the coauthor of Neural Fuzzy Systems (Prentice-Hall), and the author of Neural Fuzzy Control Systems with Structure and Parameter Learning (World Scientific). He has published more than 200 journal papers (Total Citation: 18,569, H- index: 51, i10-index: 307) in the areas of neural networks, fuzzy systems, multimedia hardware/software, and cognitive neuro-engineering, including approximately 98 IEEE journal papers.
Speaker : Prof. Derong Liu
Abstract : Adaptive dynamic programming (ADP), in particular, iterative ADP, has received increasing attention recently. ADP scheme is a design that approximates dynamic programming in the general case, i.e., approximates optimal control over time in noisy, nonlinear environments. The goal of ADP is to solve the “curse of dimensionality” problems by avoiding the backward numerical process required for its solutions. Over the past few years, progress has been made to use iterative algorithms to approximate solutions of dynamic programming. This talk will review the recent theoretical development of ADP. It will start with the basic concepts in ADP and then move on to the development of iterative ADP in areas such as robust control, tracking control, generalized policy/value iteration, and game theory. Real and potential applications of ADP will also be discussed including truck scheduling, call admission control in cellular networks, and power management in smart homes.
Biography : Derong Liu received the Ph.D. degree in electrical engineering from the University of Notre Dame in 1994. He was a Staff Fellow with General Motors Research and Development Center, from 1993 to 1995. He was an Assistant Professor with the Department of Electrical and Computer Engineering, Stevens Institute of Technology, from 1995 to 1999. He joined the University of Illinois at Chicago in 1999, and became a Full Professor of Electrical and Computer Engineering and of Computer Science in 2006. He was selected for the “100 Talents Program” by the Chinese Academy of Sciences in 2008, and he served as the Associate Director of The State Key Laboratory of Management and Control for Complex Systems at the Institute of Automation, from 2010 to 2015. He is now a Full Professor with the School of Automation and Electrical Engineering, University of Science and Technology Beijing. He has published 15 books (six research monographs and nine edited volumes). He is an elected AdCom member of the IEEE Computational Intelligence Society, he is the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems, and he is the Editor-in-Chief of Artificial Intelligence Review (Springer). He was the General Chair of 2014 IEEE World Congress on Computational Intelligence and is the General Chair of 2016 World Congress on Intelligent Control and Automation. He received the Faculty Early Career Development Award from the National Science Foundation in 1999, the University Scholar Award from University of Illinois from 2006 to 2009, the Overseas Outstanding Young Scholar Award from the National Natural Science Foundation of China in 2008, and the Outstanding Achievement Award from Asia Pacific Neural Network Assembly in 2014. He is a Fellow of the IEEE and a Fellow of the International Neural Network Society.
Speaker : Prof. Kay Chen Tan
Abstract : Multi-objective optimization is widely found in many fields, such as logistics, economics, engineering, or whenever optimal decisions need to be made in the presence of trade-offs. The problem is challenging because it involves the simultaneous optimization of several conflicting objectives in the Pareto optimal sense and requires researchers to address many issues that are unique to MO problems. This talk will provide an overview of evolutionary computation for multi-objective optimization (EMO). It will then present an application of EMO on solving BCI applications. Future challenges and directions in the field of EMO will also be discussed.
Biography : Dr Kay Chen Tan received the B.Eng. (Hons.) degree in electronics and electrical engineering and the Ph.D. degree from University of Glasgow, Glasgow, U.K., in 1994 and 1997, respectively. He is an Associate Professor with the Department of Electrical and Computer Engineering, National University of Singapore, Singapore. He is actively pursuing research in computational and artificial intelligence, with applications to multi-objective optimization, scheduling, data analytics, prognostics, BCI etc. Dr Tan has published over 120 journal papers, over 120 papers in conference proceedings, co-authored 5 books. He has been an Invited Keynote/Plenary speaker for over 50 international conferences. He was the General Co-Chair for IEEE Congress on Evolutionary Computation 2007 in Singapore and is the General Co-Chair for IEEE World Congress on Computational Intelligence 2016 in Vancouver, Canada. Dr Tan is currently an elected member of AdCom (2014-2016) and is an IEEE Distinguished Lecturer of IEEE Computational Intelligence Society (2011-2013; 2015-2017). Dr Tan is a Fellow of IEEE. He is also the Editor-in-Chief of IEEE Transactions on Evolutionary Computation. He served as the Editor-in-Chief of IEEE Computational Intelligence Magazine (2010-2013), and currently serves as an Associate Editor / Editorial Board member of over 20 international journals, such as IEEE Transactions on Cybernetics, IEEE Transactions on Computational Intelligence and AI in Games, Evolutionary Computation (MIT Press), European Journal of Operational Research, Neural Computing and Applications, Journal of Scheduling, International Journal of Systems Science, etc. He is the awardee of the 2012 IEEE Computational Intelligence Society (CIS) Outstanding Early Career Award for his contributions to evolutionary computation in multi-objective optimization. He also received the 2016 IEEE CIS Outstanding TNNLS Paper Award for his paper titled "Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons". He also received the Recognition Award (2008) from the International Network for Engineering Education & Research (iNEER) for his outstanding contributions to engineering education and research. He was felicitated by the International Neural Network Society (INNS) India Regional Chapter (2014) for his outstanding contributions in the field of computational intelligence.
Speaker : Prof. Jun Wang
Abstract : In the present information era, huge amount of data to be processed daily. In contrast of conventional sequential data processing techniques, parallel data processing approaches can expedite the processes and more efficiently deal with big data. In the last few decades, neural computation emerged as a popular area for parallel and distributed data processing. The data processing applications of neural computation included, but not limited to, data sorting, data selection, data mining, data fusion, and data reconciliation. In this talk, neurodynamic approaches to parallel data processing will be introduced, reviewed, and compared. In particular, my talk will compare several mathematical problem formulations of well-known multiple winners-take-all problem and present several recurrent neural networks with reducing model complexity. Finally, the best one with the simplest model complexity and maximum computational efficiency will be highlighted. Analytical and Monte Carlo simulation results will be shown to demonstrate the computing characteristics and performance of the continuous-time and discrete-time models. The applications to parallel sorting, rank-order filtering, and data retrieval will be also discussed.
Biography : Jun Wang is a Professor at the Chinese University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, and University of North Dakota. He also held various short-term visiting positions at USAF Armstrong Laboratory (1995), RIKEN Brain Science Institute (2001), University Catholique de Louvain (2001), Chinese Academy of Sciences (2002), Huazhong University of Science and Technology (2006–2007), and Shanghai Jiao Tong University (2008-2011) as a Changjiang Chair Professor. Since 2011, he is a National Thousand-Talent Chair Professor at Dalian University of Technology on a part-time basis. He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology, Dalian, China. He received his Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, Ohio, USA. His current research interests include neural networks and their applications. He published over 180 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 since 2014 and a member of the editorial board of Neural Networks since 2012. 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 advisory board of International Journal of Neural Systems (2006-2013), as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), Neurocomputing (2008, 2014), and International Journal of Fuzzy Systems (2010, 2011). 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 (2011-2012); IEEE Computational Intelligence Society Awards Committee (2008, 2012, 2014), IEEE Systems, Man, and Cybernetics Society Board of Directors (2013-2015), 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, Natural Science Awards from Shanghai Municipal Government (2009) and Ministry of Education of China (2011), and Neural Networks Pioneer Award from IEEE Computational Intelligence Society (2014), among others.
Speaker : Prof. Zhifeng Hao
Abstract : Smart city construction has become great needs of society progress and development. Large scale information processing and intelligent computing technologies are the key to achieve the goals of smart city. Big data in smart city are difficult to be sensed, and are collected from multiple channels. Considering such unique challenges, we present some of the current studies in this talk: to build neural sensing network in smart city and study multi-network based collaborative sensing and data quality enhancement technologies, to achieve deep sensing of the city environments; to study object-behavior recognition, abnormal events detection and event relation discovery, to achieve the accurate pattern recognition of the massive multi-channel smart city information; to investigate large scale event causality discovery, situation analysis and causal reasoning based decision support technologies, to realize the real-time and effective decision of the smart cities; to conduct prototype based verification of the technologies, to study the effectiveness of the deep sensing theory, the accuracy of the pattern recognition method on the massive multi-channel smart city information, and the timeliness of the decision strategy on the large-scale dynamic smart city events.
Biography : Prof. Zhifeng Hao received the B.S. degree in Mathematics from the Sun Yat-Sen University in 1990, and the Ph.D. degree in Mathematics from Nanjing University in 1995. He has been serving as the President of Foshan University since 2015, and a full Professor and Doctoral supervisor at South China University of Technology and Guangdong University of Technology. Professor Hao currently serves as the member of Teaching Guiding Committee for Mathematics and Statistics under the Ministry of Education, member of a council in Chinese Society for industrial and Applied Mathematics, vice-chairman of Association for the promotion of Towns of Industry Clusters of Guangdong province (POTIC) and the director-general of the Supercomputing Alliance of Guangdong province. As a renowned scholar, Professor Hao was awarded the Ding Ying Science and Technology Award by Guangdong province and the “May4th” Youth medal; he has received a second prize of Ministry of Education Natural Science Award, two second prizes of Natural Science Award of Guangdong province, a second prize of the Sixth Young Teachers in Higher Education Institutions Sponsored by National Board of Education Henry Fok Education Foundation. Professor Hao has been selected for the New Century Excellent Talents in University (NCET) program which is funded by the Ministry of Education; he has been selected as Cultivated Talent by the “Thousand-Hundred-Ten” Program of Guangdong Province. Professor Hao is also an expert with State Council special allowance for his outstanding contributions. He has a variety of research interests which include bioinformatics, kernel learning, nonlinear optimization, evolutionary algorithms and intelligence computation. He has published more than 50 research papers in vaproceedingrious refereed international journals.