2016～2017学年第一学期第十一周学术活动安排5

欢迎广大师生前往！

                                                                                                                             校科协、计算机学院

                                                                                                            2016年11月4日

报告人简历：

Haibo 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/

报告摘要：

With the recent development of brain research and modern technologies, scientists and engineers will hopefully find efficient ways to develop brain-like intelligent systems that are highly robust, adaptive, scalable, and fault tolerant to uncertain and unstructured environments. Yet, developing such truly intelligent systems requires significant research on both fundamental understanding of brain intelligence as well as complex engineering design. This talk aims to present the recent research developments in computational intelligence to advance the machine intelligence research and explore their wide applications in complex systems across different engineering domains.

Specifically, this talk covers numerous aspects of the foundations and architectures of adaptive learning and control. The key objective is to achieve cognitive-alike optimization and prediction capability through learning. An essential component of this talk is a recent development of a new adaptive dynamic programing (ADP) architecture for improved learning and optimization capability over time. This architecture integrates a hierarchical goal generator network to provide the system a more informative and detailed internal goal representation to guide its decision-making. Various complex system applications including smart grid and human-robot interaction will be presented to demonstrate the broader and far-reaching applications of our research. As a multi-disciplinary research topic, I will also briefly discuss the future research challenges and opportunities in this field. Finally, as the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS, latest impact factor: 4.854), I will give an overview of the current status of the TNNLS, how to publish in TNNLS, and the common issues in the publication process. The key is to provide a comprehensive analysis of the entire publication procedure, ranging from research and paper development, submission, review, author response, to the final decision making procedure.