Invited Talks

Zhi-Hua Zhou

Nanjing University

Title: Deep Forest: An exploration to non-NN style deep learning

Date/time: 08:30am - 09:30am

Room: Ming-Xiao(明宵厅)

Abs:
In this talk, we will discuss about some essentials in deep learning, and claim that deep learning is not necessarily to be realized by neural network models. We will present gcForest, a decision tree ensemble approach with performance highly competitive to deep neural networks in a broad range of tasks. In contrast to deep neural networks which require great effort in hyper-parameter tuning, gcForest is much easier to train; even when it is applied to different data across different domains in our experiments, excellent performance can be achieved by almost same settings of hyper-parameters.
Bio:
Zhi-Hua Zhou is a Professor and Founding Director of the LAMDA Group at Nanjing University. His main research interests are in artificial intelligence, machine learning and data mining. He authored the books "Ensemble Methods: Foundations and Algorithms" and "Machine Learning (in Chinese)", and published more than 200 papers in top-tier international journals/conferences. According to Google Scholar, his publications have received more than 30,000 citations, with an H-index of 82. He also holds 22 patents and has good experiences in industrial applications. He has received various awards, including the National Natural Science Award of China, PAKDD Distinguished Contribution Award, IEEE ICDM Outstanding Service Award, Microsoft Professorship Award, etc. He serves as the Executive Editor-in-Chief of Frontiers of Computer Science, Associate Editor-in-Chief of Science China Information Science, and Associate Editor of Machine Learning, IEEE Trans Pattern Analysis and Machine Intelligence, ACM Trans Knowledge Discovery from Data, etc. He founded ACML (Asian Conference on Machine Learning) and served as General co-chair of IEEE ICDM 2016, Program co-chair of IJCAI 2015 Machine Learning track, etc. He will serve as Program co-chair of AAAI 2019 and IJCAI 2021. He also serves as the Chair of the CCF-AI, and was Chair of the IEEE CIS Data Mining Technical Committee. He is a foreign member of the Academy of Europe, and Fellow of the ACM, AAAI, AAAS, IEEE, IAPR, CCF, and CAAI

Kay Chen TAN

City University of Hong Kong

Title: Applications of Computational Intelligence in Condition-Based Maintenance

Date/time: 10:00am - 11:00am

Room: Ming-Xiao(明宵厅)

Abs:
Condition-based maintenance (CBM) is an important tool for running a plant or factory in an optimal manner. Better operations will lead to lower production cost and lower use of resources. Data-driven approaches which do not rely on the domain knowledge are popular in solving CBM problems. This talk will provide an overview of computational intelligence in the application of CBM such as robust prognostic and automated surface inspection. As one of the key enablers of condition-based maintenance, prognostic involves the core task of determining the remaining useful life of a system. This talk will discuss the use of deep learning ensembles to improve the prediction accuracy of remaining useful life estimation. A case study involving automated surface inspection and the estimation of remaining useful life for turbofan engines will also be presented.
Bio:
Prof. TAN Kay Chen received the B.Eng. degree (First Class Hons.) and the Ph.D. degree from the University of Glasgow, U.K., in 1994 and 1997, respectively. He is currently a Professor with the Department of Computer Science, City University of Hong Kong, Hong Kong. He has published over 130 journal papers and over 130 papers in conference proceedings, and co-authored six books. His current research interests include computational intelligence and its applications to evolutionary multi-objective optimization, data analytics, prognostics, BCI, and operational research etc.

He is the Editor-in-Chief of IEEE Transactions on Evolutionary Computation (IF: 10.629), was the EiC of IEEE Computational Intelligence Magazine (2010-2013), and currently serves on the Editorial Board of over 10 international journals such as IEEE Transactions on Cybernetics, IEEE Transactions on Computational Intelligence and AI in Games, Evolutionary Computation (MIT Press) etc. He has been an invited Keynote/Plenary speaker for over 60 international conferences and was the General Co-Chair for IEEE World Congress on Computational Intelligence (WCCI) 2016 in Vancouver, Canada. He also serves as the General Co-Chair for IEEE Congress on Evolutionary Computation (CEC) 2019 in Wellington, New Zealand.

He is a Fellow of IEEE, an elected AdCom member of IEEE Computational Intelligence Society (2014-2019), and an IEEE Distinguished Lecturer (2011-2013; 2015-2017). He was a recipient of the 2016 IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Awards. He was also the awardee of the 2012 IEEE Computational Intelligence Society 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.


Jun WANG

City University of Hong Kong

Title: Collaborative Neurodynamic Optimization: Biologically and Socially Plausible Approaches to Distributed, Global and Multiple-objective Optimization

Date/time: 11:00am - 12:00am

Room: Ming-Xiao(明宵厅)

Summary:
The past three decades witnessed the birth and growth of neurodynamic optimization which has emerged and matured as a powerful approach to real-time optimization due to its inherent nature of parallel and distributed information processing and the hardware realizability. Despite the success, almost all existing neurodynamic approaches work well only for convex and generalized-convex optimization problems with unimodal objective functions. Effective neurodynamic approach to constrained global optimization with multimodal objective functions is rarely available. In this talk, starting with the idea and motivation of neurodynamic optimization, I will review the historic review and present the state of the art of neurodynamic optimization with many individual models for convex and generalized convex optimization. In addition, I will present a multiple-time-scale neurodynamic approach to selected constrained optimization. Finally, I will introduce population-based collaborative neurodynamic approaches to constrained distributed and global optimization. By deploying a population of individual neurodynamic models with diversified initial states at a lower level coordinated by using some global search and information exchange rules (such as PSO) at a upper level, it will be shown that global and multi-objective optimization problems can be solved effectively and efficiently.
Bio:
Jun Wang is a Chair Professor of Computational Intelligence in the Department of Computer Science at City University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, University of North Dakota, and Chinese University of Hong Kong. He also held various part-time visiting positions at US Air Force Armstrong Laboratory, RIKEN Brain Science Institute, Huazhong University of Science and Technology, Dalian University of Technology, and Shanghai Jiao Tong University as a Changjiang Chair Professor. 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 about 200 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. 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), and a member of the editorial board of Neural Networks (2012-2014) as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), Neurocomputing (2008, 2014, 2016), 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.

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