Keynote Speakers
August 20-23, 2015
Fuzhou, China
(http://www.ic-ic.org/2015/index.htm)

Abstract: The widespread deployment of visual sensors such as surveillance cameras leads to the explosion of visual data. The timely processing and understanding of such massive information presents a clear challenge for intelligent computing. This talk starts with a brief introduction to the concept of large-scale visual computing and outlines the status quo of the field as well as the main research challenges. It focuses on discussing some of the key issues in large-scale visual computing such as large-scale feature representation, large-scale modelling and knowledge transferring. It also discusses some promising directions for future research.

Bio-Sketch: Tieniu Tan received his B.Sc. degree in electronic engineering from Xi'an Jiaotong University, China, in 1984, and his MSc and PhD degrees in electronic engineering from Imperial College London, U.K., in 1986 and 1989, respectively.
    In October 1989, he joined the Department of Computer Science, The University of Reading, U.K., where he worked as a Research Fellow, Senior Research Fellow and Lecturer. In January 1998, he returned to China to join the National Laboratory of Pattern Recognition (NLPR), Institute of Automation of the Chinese Academy of Sciences (CAS) as a full professor. He was the Director General of the CAS Institute of Automation from 2000-2007, and the Director of the NLPR from 1998-2013. He is currently Director of the Center for Research on Intelligent Perception and Computing at the Institute of Automation and also serves as Deputy Secretary-General of the CAS and the Director General of the CAS Bureau of International Cooperation. He has published more than 450 research papers in refereed international journals and conferences in the areas of image processing, computer vision and pattern recognition, and has authored or edited 11 books. He holds more than 70 patents. His current research interests include biometrics, image and video understanding, and information forensics and security.
    Dr Tan is a Member (Academician) of the Chinese Academy of Sciences, Fellow of The World Academy of Sciences for the advancement of sciences in developing countries (TWAS), an International Fellow of the UK Royal Academy of Engineering, and a Fellow of the IEEE and the IAPR (the International Association of Pattern Recognition). He is Editor-in-Chief of the International Journal of Automation and Computing. He has given invited talks and keynotes at many universities and international conferences, and has received numerous national and international awards and recognitions.

Abstract: Online feature selection with streaming features refers to applications where the knowledge of the full feature space is unknown in advance and features flow in one by one over time. This is in contrast with traditional online learning methods that only deal with sequentially added data instances, with little attention being paid to streaming features. The critical challenges for online streaming feature selection include (1) the continuous growth of feature volumes over time, (2) a large feature space, possibly of unknown or infinite size, and (3) the unavailability of the entire feature set before learning starts. This talk introduces our recent research efforts on online streaming feature selection to select strongly relevant and non-redundant features on the fly.

Bio-Sketch: Xindong Wu is a Yangtze River Scholar in the School of Computer Science and Information Engineering at the Hefei University of Technology (China), a Professor of Computer Science at the University of Vermont (USA), and a Fellow of the IEEE and AAAS. He received his Bachelor's and Master's degrees in Computer Science from the Hefei University of Technology, China, and his Ph.D. degree in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems, and Web information exploration.
    Dr. Wu is the Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), the Editor-in-Chief of Knowledge and Information Systems (KAIS, by Springer), and an Editor-in-Chief of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP). He was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE, by the IEEE Computer Society) between 2005 and 2008. He served as Program Committee Chair/Co-Chair for ICDM '03 (the 2003 IEEE International Conference on Data Mining), KDD-07 (the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), CIKM 2010 (the 19th ACM Conference on Information and Knowledge Management), and IEEE/ACM ASONAM '14 (the 2014 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining). He received the 2012 IEEE Computer Society Technical Achievement Award "for pioneering contributions to data mining and applications", and the 2014 IEEE ICDM 10-Year Highest-Impact Paper Award.

Abstract: Clustering validation techniques are important for comparing the results of different algorithms and they are frequently necessary for discussions with domain experts. They also present opportunities for research innovations in and of themselves. Examples of these techniques and their use are presented.

Bio-Sketch: Donald Wunsch is the Mary K. Finley Missouri Distinguished Professor at Missouri University of Science & Technology (Missouri S&T). Earlier employers were: Texas Tech University, Boeing, Rockwell International, and International Laser Systems. His education includes: Executive MBA - Washington University in St. Louis, Ph.D., Electrical Engineering-University of Washington (Seattle), M.S., Applied Mathematics (same institution), B.S., Applied Mathematics - University of New Mexico, and Jesuit Core Honors Program, Seattle University. Key research contributions are: Clustering; Adaptive Resonance and Reinforcement Learning architectures, hardware and applications; Neurofuzzy regression; Traveling Salesman Problem heuristics; Robotic Swarms; and Bioinformatics. He is an IEEE Fellow and previous INNS President, INNS Fellow and Senior Fellow 2007-2013, NSF CAREER Award winner, and winner of the 2015 INNS Gabor Award. He served as IJCNN General Chair, and on several Boards, including the St. Patrick’s School Board, IEEE Neural Networks Council, International Neural Networks Society, and the University of Missouri Bioinformatics Consortium, Chaired the Missouri S&T Information Technology and Computing Committee as well as the Student Design and Experiential Learning Center Board. He has produced 16 Ph.D. recipients in Computer Engineering, Electrical Engineering, and Computer Science; has attracted over $8 million in sponsored research; and has over 300 publications including nine books. His research has been cited over 10,000 times.