University of Technology Sydney
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 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: 14,865, H-index: 55, i10-index: 186) in the areas of neural networks, fuzzy systems, multimedia hardware/software, and cognitive neuro-engineering, including over 100 IEEE journal papers.
COMPUTATIONAL INTELLIGENCE FOR 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.
University of Cyprus
Marios M. Polycarpou is a Professor of Electrical and Computer Engineering and the Director of the KIOS Research Center for Intelligent Systems and Networks at the University of Cyprus. He received undergraduate degrees in Computer Science and in Electrical Engineering, both from Rice University, USA in 1987, and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Southern California, in 1989 and 1992 respectively. His teaching and research interests are in intelligent systems and networks, adaptive and cooperative control systems, computational intelligence, fault diagnosis and distributed agents. Dr. Polycarpou has published more than 300 articles in refereed journals, edited books and refereed conference proceedings, and co-authored 7 books. He is also the holder of 6 patents.
Prof. Polycarpou is a Fellow of IEEE and IFAC, and past IEEE Distinguished Lecturer of Computational Intelligence. He is the recipient of the 2016 IEEE Neural Networks Pioneer Award and the 2014 Best Paper Award for the journal Building and Environment (Elsevier). He has served as the President of the IEEE Computational Intelligence Society (2012-2013), and as the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems (2004-2010). He is currently the Vice President of the European Control Association (EUCA). Prof. Polycarpou has participated in more than 60 research projects/grants, funded by several agencies and industry in Europe and the United States, including the prestigious European Research Council (ERC) Advanced Grant.
Smart Buildings: a Platform for Computational Intelligence
Abstract: Modern buildings are complex systems of structures and technology aimed at providing a safe and comfortable environment for the occupants. Recent advances in information and communication technologies have generated significant interest in developing smart buildings, which provide much greater capabilities in terms of energy efficiency, safety, security, interactivity, as well as in terms of mitigating environmental impact. New components for smart buildings, such as sensors, actuators, controllers, embedded systems and wireless communications, are becoming readily available. Moreover, the Internet-of-Things (IoT) technology is already having a significant impact on developments related to smart buildings. The objective of this presentation is to provide an overview of current advances in smart buildings and key challenges in the years ahead. Specific emphasis is given to issues related to the “intelligence” inside the smart buildings, and how they can serve as a platform for computational intelligence concepts and methodologies. Various estimation, learning and feedback control algorithms will be presented and illustrated, and directions for future research will be discussed.
IBM Watson Yorktown Heights, New York, USA
Dr. Lazaros Poymenakos is a senior research scientist in the area of Cognitive Dialog Systems at IBM T. J. Watson Research Center with focus on advanced machine learning for end-to-end dialog systems and knowledge representation. His research interests span the areas on Human-Computer Interaction, Deep Learning, Intelligent Systems and Perceptual Interfaces. He received his Ph.D. and Master degrees from MIT (’95, ’92 respectively) and his undergraduate from NTUA (’89, top in class). Prior to IBM, he held several positions in academia and in the industry both in the USA and in Europe and he has been the inceptor and prime investigator for several large funded research projects with strong scientific impact. His research and academic work has led to more than 40 publications in international scientific journals, a book, and more than 50 papers in reviewed international conferences. He is the lead inceptor in 4 international patents and has served on the organizing committees of several international conferences.
Cognitive Computing: From the Lab to the Field
Abstract: The era of AI breakthroughs has arrived. More so, there are applications that are directly going to benefit from them. Congnitive computing is enabled by large amounts of data that are used as the basis for learning. In turn, the resulting AI systems make sense of the data, discover new relations and facts, help decision making and comparisons. Moreover, the AI systems are starting to deal with language understanding, goal driven dialog, and reasoning. Key areas of application span the whole industry but can make an impact in areas deeply affecting human lives like healthcare and education. We will explore recent breakthroughs in AI and their direct application to different segments of the industry.
Honda Research Institute Europe
Bernhard Sendhoff obtained a PhD in Applied Physics in May 1998, from the Ruhr-Universität Bochum, Germany. From 1999 to 2002 he worked for Honda R&D Europe (Deutschland) GmbH last in the position of Deputy Division Manager and Chief Scientist. In 2003, he joined the Honda Research Institute Europe GmbH as Chief Technology Officer. Since 2007 he is Honorary Professor of the School of Computer Science of the University of Birmingham, Great Britain and since 2008, he is Honorary Professor at the Technical University of Darmstadt, Germany. Since 2011 he is President of the Honda Research Institute Europe GmbH. Bernhard Sendhoff is a senior member of the IEEE and the ACM and a member of the SAE. He has authored or co-authored more than 180 scientific publications.
Cooperative Intelligence - Beyond Autonomy
Cooperative intelligence describes the capability of systems to work closely together with humans in a variety of ways and with different emphasis towards solving a complex task in a demanding environment. Whereas a large part of the recent substantial advances in computational and artificial intelligence are related to a larger degree of system's autonomy (e.g. in autonomous driving), I want to emphasize both conceptual as well as practical research questions that are important for building intelligent systems that cannot just work for us but also among us and with us in the future. Cooperation can be realized for the benefit of the machine or for the benefit of the human - in my talk I will concentrate on the later part. After a conceptual introduction into cooperative intelligence, I will focus on the topics of prediction, novelty and learning in the context of cooperative intelligence. The prediction of the behavior of others in any situation is relevant for the artificial system to make decisions that are socially compliant, while at the same time behavior that is derived from a high-level prediction can be used for communication. I will show in a practical application that even for a situation with limited behavioral complexity - a motorway scenario - successful prediction can make a big difference and enables cooperative behavior with other traffic participants. Cooperative systems in engineering often need a different kind of prediction, i.e. the system has to reach an understanding of abstract and to some degree personalized notions like novelty or interestingness of solutions or designs. The area of machine learning has witnessed a tremendous increase in interest through the use of deep network structures and related new learning paradigms. Learning in interaction is a necessary prerequisite to enable cooperation. Finally, for intelligent systems to reach a higher level of cooperation it is necessary to establish concepts of confidence and trust between humans and intelligent systems. I will finish my talk with a brief discussion of such issues leading eventually to the idea of a hybrid society.
Kay Chen Tan
National University of Singapore
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 currently with the Department of Electrical and Computer Engineering, National University of Singapore, Singapore. He is actively pursuing research in computational intelligence, with applications to multi-objective optimization, scheduling, data analytics, prognostics, BCI etc.
Dr Tan has published over 250 journal and conference papers and 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 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.
EC at Work: Opportunities and Challenges
Abstract: Evolutionary Computing (EC), which is based on the principles of natural selection and genetic inheritance, is often considered a global optimization methodology with a metaheuristic or stochastic optimization character. It is distinguished by the use of a population of candidate solutions rather than traditional approach of iterating over a single point in the search space. EC is being increasingly applied to many problems, ranging from practical applications in industry to cutting-edge scientific research. The plenary will provide a brief overview of this exciting research field including opportunities and challenges faced in applying EC to a variety of real-world multi-objective problems, such as design automation, robust optimization and logistic application. In particular, a case study involving the estimation of remaining useful life (RUL) for turbofan engines in the area of robust prognostic will be studied. As one of the key enablers of condition-based maintenance, prognostic involves the core task of determining the RUL of the system. The plenary will also present an application of evolutionary deep learning ensembles to improve the prediction accuracy of RUL estimation for turbofan engines.