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Agenda

Time/Date July 4th
(Monday)
July 5th
(Tuesday)
July 6th
(Wednesday)
8:30 – 10:30 Registration Plenary Lecture 4 Plenary Lecture 8
10:30 - 11:00 Coffee Break
11:00 – 12:30 Plenary Lecture 1 Plenary Lecture 5 Plenary Lecture 9
12:30 – 14:00 Lunch Break
14:00 - 15:30 Plenary Lecture 2 Plenary Lecture 6 Lab Projects
15:30 – 16:00 Coffee Break
16:00 – 17:30 Plenary Lecture 3 Plenary Lecture 7 Lab Projects
18:30 – 19:30 Closing Ceremony
19:30 - 21:00


Talks and Speakers’ Details

  • Prof. Gary G. Yen

    Prof. Yen received his PhD degree in electrical and computer engineering from the University of Notre Dame in 1992. He worked at the Structural Control Division of the USAF Research Laboratory in Albuqurque during 1992-1996. He is currently a Professor in the School of Electrical and Computer Engineering, Oklahoma State University in Stillwater. His research is supported by the DoD, DoE, EPA, NASA, NSF, and Process Industry. His research interest includes intelligent control, computational intelligence, conditional health monitoring, signal processing and their industrial/defense applications.
    Prof. Yen was an associate editor of the IEEE Control Systems Magazine, IEEE Transactions on Control Systems Technology, Automatica, Mechantronics, IEEE Transactions on Systems, Man and Cybernetics, Part A and Part B, IEEE Transactions on Neural Networks, and among others. He is currently serving as an associate editor for the IEEE Transactions on Evolutionary Computation and International Journal of Swarm Intelligence Research. He served as the General Chair for the 2003 IEEE International Symposium on Intelligent Control held in Houston and 2006 IEEE World Congress on Computational Intelligence held in Vancouver. Dr. Yen served as Vice President for the Technical Activities in 2005-2006 and President in 2010-2011 of the IEEE Computational Intelligence Society. He is the founding Editor-in-Chief of the IEEE Computational Intelligence Magazine.
    He received KC Wong Fellowship from the Chinese Acadamy of Sciences, Halliburton Outstanding Faculty award, and OSU Regents Distinguished Research award. He also received an Honorary Professorship from Northeastern University, Sichuan University, and Dalian University of Technology in China. In 2011, he received the Andrew P Sage Best Transactions Paper award from the IEEE Systems, Man and Cybernetics Society. In 2013, he received Meritorious Service award from the IEEE Computational Intelligence Society. He is a distinguished lecturer from the IEEE Computational Intelligence Society, 2012-2014, an IEEE Fellow, and IET Fellow.
    Talk: State-of-the art Evolutionary Many-Objective Optimization Algorithms
    Abstract: Evolutionary computation is the study of biologically motivated computational paradigms which exert novel ideas and inspiration from natural evolution and adaptation. The applications of population-based heuristics in solving multiobjective optimization problems have been receiving a growing attention. To search for a family of Pareto optimal solutions based on nature-inspiring problem solving paradigms, Evolutionary Multiobjective Optimization Algorithms have been successfully exploited to solve optimization problems in which the fitness measures and even constraints are uncertain and changed over time. When encounter optimization problems with many objectives, nearly all designs performs poorly because of loss of selection pressure in fitness evaluation solely based upon Pareto optimality principle. This talk will survey recently published literature along this line of research- evolutionary algorithm for many-objective optimization and its real-world applications. Based on performance metrics ensemble, we will provide a comprehensive measure among all competitors and more importantly reveal insight pertaining to specific problem characteristics that the underlying evolutionary algorithm could perform the best. The experimental results confirm the finding from the No Free Lunch theorem: any algorithm’s elevated performance over one class of problems is exactly paid for in loss over another class.
  • Prof. Chin-Teng Lin

    Prof. 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 Faculty of Engineering and Information Technology, University of Technology Sydney, Chair Professor of Electrical and Computer Engineering, NCTU, International Faculty of University of California at San-Diego (UCSD), 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 (CIS) in 2008-2010, and Chair of IEEE Taipei Section in 2009-2010. Dr. Lin is the Distinguished Lecturer of IEEE CAS Society from 2003 to 2005, and CIS Society from 2015-2017. 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: 20,155, H-index: 53, i10-index: 373) in the areas of neural networks, fuzzy systems, multimedia hardware/software, and cognitive neuro-engineering, including approximately 101 IEEE journal papers.
    Talk: Computational Intelligence and Brain Computer Interface
    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.
  • Prof. Yaochu Jin

    Prof. Jin received the B.Sc., M.Sc., and Ph.D. degrees from Zhejiang University, Hangzhou, China, in 1988, 1991, and 1996 respectively, and the Dr.-Ing. degree from Ruhr University Bochum, Germany, in 2001.
    He is a Professor of Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K., where he heads the Nature Inspired Computing and Engineering Group. He is also a Finland Distinguished Professor funded by the Finnish Funding Agency for Innovation (Tekes) and a Changjiang Distinguished Visiting Professor appointed by the Ministry of Education, China. His science-driven research interests lie in the interdisciplinary areas that bridge the gap between computational intelligence, computational neuroscience, and computational systems biology. He is also particularly interested in nature-inspired, real-world driven problem-solving.  He has (co)authored over 200 peer-reviewed journal and conference papers and been granted eight patents on evolutionary optimization. His current research is funded by EC FP7, UK EPSRC and industry.
    He is the Editor-in-Chief of the IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS and Complex & Intelligent Systems. He is also an Associate Editor or Editorial Board Member of the IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, IEEE TRANSACTIONS ON CYBERNETICS, IEEE TRANSACTIONS ON NANOBIOSCIENCE, Evolutionary Computation, BioSystems, Soft Computing, and Natural Computing.
    Prof Jin was an IEEE Distinguished Lecturer (2013-2015) and Vice President for Technical Activities of the IEEE Computational Intelligence Society (2014-2015). He was the recipient of the Best Paper Award of the 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology and the 2014 IEEE Computational Intelligence Magazine Outstanding Paper Award. He is a Fellow of IEEE.
    Talk: Data-Driven Optimization of Complex Systems
    Abstract:Data-driven decision-making and optimization is becoming increasingly important in industry. In this talk, we will discuss two typical challenges arising in data-driven optimization, namely big data and sparse data. In the former situation, the available data is subject to various amount of uncertainty and the volume of data is huge, while in the latter case, only little data is available for making decisions. Techniques for handing Big Data and sparse data will then be presented to address the above mentioned challenges illustrated by real-world optimization examples.
  • Prof. Kay Chen Tan

    Prof. Tan received the B. Eng degree with First Class Honors in Electronics and Electrical Engineering, and the  Ph.D. degree from the University of Glasgow, Scotland, in 1994 and 1997, respectively. He is actively pursuing research in computational and artificial intelligence, with applications to multi-objective optimization, scheduling, automation, data mining, and games.
    Prof Tan has published over 100 journal papers, over 100 papers in conference proceedings, co-authored 5 books including Multiobjective Evolutionary Algorithms and Applications (Springer-Verlag, 2005), Modern Industrial Automation Software Design (John Wiley, 2006; Chinese Edition, 2008),  Evolutionary Robotics: From Algorithms to Implementations (World Scientific, 2006; Review), Neural Networks: Computational Models and Applications  (Springer-Verlag, 2007), and Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms (Springer-Verlag, 2009),  co-edited 4 books including Recent Advances in Simulated Evolution and Learning (World Scientific, 2004), Evolutionary Scheduling (Springer-Verlag,  2007), Multiobjective Memetic Algorithms (Springer-Verlag, 2009), and Design and Control of Intelligent Robotic Systems (Springer-Verlag, 2009).
    Dr Tan has been an Invited Keynote/Plenary speaker for over 40 international conferences. He served in the international program committee for over 100 conferences and involved in the organizing committee for over 50 international conferences, including the General Co-Chair for IEEE Congress on Evolutionary Computation 2007 in Singapore. Dr Tan 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).
    Prof Tan is the Editor-in-Chief of IEEE Transactions on Evolutionary Computation. He was the Editor-in-Chief of IEEE Computational Intelligence Magazine (2010-2013). He 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.
    Prof Tan is a Fellow of IEEE. 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 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.
    Talk: Advances in Computational Intelligence and Applications
    Abstract:The rapid development of CI research in recent years is a response to the evolution of machine intelligence in systems and to the acceleration in the use of learning and intelligent technologies in the conception and design of systems. This talk will provide an introduction to various CI technologies including evolutionary algorithms, neural networks, and fuzzy systems. Practical applications of computational intelligence in solving scientific optimization and machine learning problems, such as decision-making, control and classification, will also be highlighted.
  • Prof. Haibo He

    Prof. He is the Robert Haas Endowed Chair Professor and the Director of the Computational Intelligence and Self-Adaptive (CISA) Laboratory at the University of Rhode Island, Kingston, RI, USA. His primary research interests include computational intelligence, machine learning and data mining, cyber security, and various application domains. He has published one sole-author book (Wiley), edited 1 book (Wiley-IEEE) and 6 conference proceedings (Springer), and authored/co-authors over 200 peer-reviewed journal and conference papers, including several highly cited papers in IEEE Transactions on Neural Networks and IEEE Transactions on Knowledge and Data Engineering, Cover Page Highlighted paper in IEEE Transactions on Information Forensics and Security, and Best Readings of the IEEE Communications Society. He has delivered more than 40 invited talks around the globe. He was the Chair of IEEE Computational Intelligence Society (CIS) Emergent Technologies Technical Committee (ETTC) (2015) and the Chair of IEEE CIS Neural Networks Technical Committee (NNTC) (2013 and 2014). He served as the General Chair of 2014 IEEE Symposium Series on Computational Intelligence (IEEE SSCI’14, Orlando, Florida). He is currently the Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems. He was a recipient of the IEEE International Conference on Communications (ICC) “Best Paper Award” (2014), IEEE CIS “Outstanding Early Career Award” (2014), National Science Foundation “Faculty Early Career Development (CAREER) Award” (2011), and Providence Business News (PBN) “Rising Star Innovator” Award (2011). More information can be found at: http://www.ele.uri.edu/faculty/he/
    Talk: Imbalanced Learning in Big Data
    Abstract:Big data has become an important topic worldwide over the past several years. Among many aspects of the big data research and development, imbalanced learning has become a critical component as many data sets in real-world applications are imbalanced, ranging from surveillance, security, Internet, finance, social network, to medical and healthy related data analysis. In general, the imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews, which is a typical case for many of the medical diagnosis data analysis, such as cancerous classification. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently and effectively into information and knowledge representation.
    In this talk, I will start with an overview of the nature and foundation of the imbalanced learning, and then focus on the state-of-the-art methods and technologies in dealing with the imbalanced data, followed by a systematic discussion on the assessment metrics to evaluate learning performance under the imbalanced learning scenario. I will also present the latest research development in our group that we have developed and tested on various imbalanced data sets. Finally, as a relatively new challenge to the community, I will highlight the major opportunities and challenges, as well as potential important research directions for learning from imbalanced data facing the big data era.
  • Prof. Fuchun Sun

    孙富春,清华大学计算机科学与技术系教授,博士生导师,清华大学校学术委员会委员,计算机科学与技术系学术委员会主任,智能技术与系统国家重点实验室常务副主任。兼任担任国家863计划专家组成员,国家自然基金委重大研究计划“视听觉信息的认知计算”指导专家组成员,中国人工智能学会认知系统与信息处理专业委员会主任,中国自动化学会认知计算与系统专业委员会主任,国际刊物《IEEE Trans. on Fuzzy Systems》,《IEEE Trans. on Systems, Man and Cybernetics: Systems》《Mechatronics》和《International Journal of Control, Automation, and Systems (IJCAS)》副主编或领域主编,国际刊物《Robotics and Autonumous Systems》和《International Journal of Computational Intelligence Systems》编委,国内刊物《中国科学:F辑》和《自动化学报》编委。 98年3月在清华大学计算机应用专业获博士学位。98年1月至2000年1月在清华大学自动化系从事博士后研究,2000年至今在计算机科学与技术系工作。工作期间获得的主要奖励有:2000年全国优秀博士论文奖,2001年国家863计划十五年先进个人,2002年清华大学“学术新人奖”,2003年韩国第十八届Choon-Gang 国际学术奖一等奖第一名,2004年教育部新世纪人才奖,2006年国家杰出青年基金。获奖成果6项,两项成果获2010年教育部自然科学奖二等奖(排名第一)和2004年度北京市科学技术奖(理论类)二等奖(排名第一)、一项获2002年度教育部提名国家科技进步二等奖(排名第二)、四项获省部级科技进步三等奖。译书一部,专著两部,在国内外重要刊物发表或录用论文150余篇,其中在IEE、IEEE汇刊、Automatica、CVPR、JICAI、NIPS等国际重要刊物和计算机一区发表论文100余篇。
    Talk: 机器人视触觉认知计算
    Abstract:本报告分析了当今世界机器人的研究现状和发展趋势,指出了感知、认知和计算是未来十年乃至二十年推动机器人发展的核心动力。接着,探讨了机器人的视触觉感知、视触觉感知信息的特性分析、编码和融合等理论方法和技术。然后,介绍了报告人领导的课题组在认知传感灵巧手、面向机器人灵巧操作的视触觉信息表征、基于信息融合的生物信号处理方法和基于经验的控制理论方法方面取得最新理论成果。最后是实验结果分析和该方向的研究展望。
  • Prof. Gang Pan

    Prof. Pan is a professor of the College of Computer Science and Technology at Zhejiang University, China. He received the B.S. and Ph.D. degrees both in Computer Science from Zhejiang University in 1998 and 2004. From 2007 to 2008, he was with the University of California, Los Angeles as a visiting scholar. His interests include artificial intelligence, computer vision, and ubiquitous computing. He has published more than 100 refereed papers. He is a recipient of Microsoft Fellowship Award, New Century Excellent Talents in University, and Distinguished Young Scholars of Natural Science Fund of Zhejiang. Dr. Pan is vice-chair of ACM Hangzhou Chapter. He has served as program committee members for more than twenty prestigious international conferences, such as ICCV, CVPR, IJCAI. He is an associate editor of IEEE Systems Journal. He won two IEEE Best Paper Awards, and Honorable Mention Award of ACM UbiComp 2015.
    Talk: Towards the Convergence of Machine and Biological Intelligence
    Abstract:Recent advances in the multidisciplinary fields such as brain-machine interfaces, artificial intelligence, and computational neuroscience, signal a growing convergence between machines and living beings. Brain-machine interfaces (BMIs) enable direct communication pathways between the brain and an external device, making it possible to connect organic and computing parts at the signal level. Cyborg means a biological-machine system consisting of both organic and computing components. Cyborg intelligence aims to deeply integrate machine intelligence with biological intelligence by connecting machines and living beings via BMIs, enhancing strengths and compensating for weaknesses by combining the biological cognition capability with the machine computational capability. This talk will introduce the concept, architectures, and applications of cyborg intelligence.
  • Prof. Badong Chen

    Prof. Chen received the B.S. and M.S. degrees in control theory and engineering from Chongqing University, in 1997 and 2003, respectively, and the PhD degree in computer science and technology from Tsinghua University in 2008. He was a Post-Doctoral Researcher with Tsinghua University from 2008 to 2010, and a Post-Doctoral Associate at the University of Florida Computational NeuroEngineering Laboratory (CNEL) during the period October, 2010 to September, 2012. He visited the Nanyang Technological University (NTU) as a visiting research scientist during July to August 2015. He is currently a professor at the Institute of Artificial Intelligence and Robotics (IAIR), Xi’an Jiaotong University. His research interests are in signal processing, information theory, machine learning, and their applications in cognitive science and engineering. He has published 2 books, 3 chapters, and over 100 papers in various journals and conference proceedings. Dr. Chen is an IEEE senior member and an associate editor of IEEE Transactions on Neural Networks and Learning Systems and Journal of The Franklin Institute, and has been on the editorial board of Entropy.
    Talk: Nonlinear Statistical Similarity Measures in Kernel Space
    Abstract:Statistical similarity measures play significant roles in machine learning and signal processing . Recently, some novel similarity measures were proposed, which are defined as a certain distance in a kernel space. Typical examples include the Information Potential (IP), Cross Information Potential (CIP), Cauchy-Schwartz Divergence, Correntropy, and so on. In particular, the Correntropy as a nonlinear and local similarity measure is directly related to the probability of how similar two random variables are in a neighborhood of the joint space, controlled by the kernel bandwidth, which also has its root in Renyi's entropy (hence the name “Correntropy"). Since Correntropy (especially with a small kernel bandwidth) is insensitive to outliers, it is naturally a robust cost for machine learning. The Correntropy Induced Metric (CIM) as a nice approximation of the l0 norm can also be used as a sparsity penalty in sparse learning. This talk will give an overview of several similarity measures in kernel space, with a particular emphasis on Correntropy. The applications to robust regression, adaptive filtering, principal components analysis (PCA), deep learning and causality detection, will also be discussed. 

© 2016 Neuromorphic Computing Research Center, Sichuan University.