Keynote Speakers



Prof. Zhi-Hua Zhou
ACM, AAAI, IEEE, Fellow

Nanjing University, China

Zhi-Hua Zhou is Professor of Computer Science and Artificial Intelligence at Nanjing University. His research interests are in artificial intelligence, machine learning and data mining, with significant contributions to ensemble methods, weakly supervised learning, and multi-label learning. He has authored the books "Ensemble Methods: Foundations and Algorithms", "Machine Learning (in Chinese)", etc., and published more than 200 papers in top-tier journals or conferences. According to Google Scholar, his publications received 56,000+ citations, with H-index 105. Many of his inventions have been successfully applied in industry.

Zhou founded ACML (Asian Conference on Machine Learning), served as Program Chair for AAAI-19, IJCAI-21, etc., General Chair for ICDM'16, PAKDD'19, etc., and Senior Area Chair for NeurIPS and ICML. He is on the advisory board of AI Magazine, served as editor-in-chief for Frontiers in Computer Science, associate editor-in-chief of Science China Information Sciences, and associate editor of AIJ, MLJ, IEEE TPAMI, ACM TKDD, etc. He is a recipient of the National Natural Science Award of China, the IEEE CS Edward J. McCluskey Technical Achievement Award, the CCF-ACM Artificial Intelligence Award, the PAKDD Distinguished Contribution Award, the ACML Distinguished Contribution Award, etc., member of the Academy of Europe, and Fellow of the ACM, AAAI, AAAS, IEEE, IAPR, IET/IEE, CCF, and CAAI.

Speech Title: A New Paradigm to Leverage Machine Learning and Logical Reasoning
Abstract: To develop a unified framework which accommodates and enables machine learning and logical reasoning to work together effectively is a well-known holy grail problem in artificial intelligence. It is often claimed that advanced intelligent technologies could emerge when machine learning and logical reasoning can be seamlessly integrated as human beings generally perform problem-solving based on the leverage of perception and reasoning, where perception corresponds to a data-driven process that can be realized by machine learning whereas reasoning corresponds to a knowledge-driven process that can be realized by logical reasoning. This talk ill present a recent study in this line.




Prof. Dapeng Wu
IEEE Fellow

University of Florida, USA

Dapeng Oliver Wu received Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, PA, in 2003. Since 2003, he has been on the faculty of Electrical and Computer Engineering Department at University of Florida, Gainesville, FL, where he is currently Professor. His research interests are in the areas of networking, communications, video coding, image processing, computer vision, signal processing, and machine learning.

He received University of Florida Term Professorship Award in 2017, University of Florida Research Foundation Professorship Award in 2009, AFOSR Young Investigator Program (YIP) Award in 2009, ONR Young Investigator Program (YIP) Award in 2008, NSF CAREER award in 2007, the IEEE Circuits and Systems for Video Technology (CSVT) Transactions Best Paper Award for Year 2001, the Best Paper Award in GLOBECOM 2011, and the Best Paper Award in QShine 2006. He has served as Editor-in-Chief of IEEE Transactions on Network Science and Engineering, and Associate Editor of IEEE Transactions on Communications, IEEE Transactions on Signal and Information Processing over Networks, and IEEE Signal Processing Magazine. He was the founding Editor-in-Chief of Journal of Advances in Multimedia between 2006 and 2008, and an Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Wireless Communications and IEEE Transactions on Vehicular Technology. He has served as Technical Program Committee (TPC) Chair for IEEE INFOCOM 2012. He was elected as a Distinguished Lecturer by IEEE Vehicular Technology Society in 2016. He is an IEEE Fellow.

Speech Title: Knowledge Centric Networking: Challenges and Opportunities
Abstract: In the creation of a smart future information society, Internet of Things (IoT) and Content Centric Networking (CCN) break two key barriers for both the front-end sensing and back-end networking. However, we still observe the missing piece of the research that dominates the current design, i.e., lacking of the knowledge penetrated into both sensing and networking to glue them holistically. In this talk, I will introduce and discuss a new networking paradigm, called Knowledge Centric Networking (KCN), as a promising solution. The key insight of KCN is to leverage emerging machine learning or deep learning techniques to create knowledge for networking system designs, and extract knowledge from collected data to facilitate enhanced system intelligence and interactivity, improved quality of service, communication with better controllability, and lower cost. This talk presents the KCN design rationale, the KCN benefits and also the potential research opportunities.





Prof. James Tin-Yau Kwok
IEEE Fellow

The Hong Kong University of Science and Technology, Hong Kong, China

James Kwok is a Professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. He is an IEEE Fellow.

Prof. Kwok received his B.Sc. degree in Electrical and Electronic Engineering from the University of Hong Kong and his Ph.D. degree in computer science from the Hong Kong University of Science and Technology. He then joined the Department of Computer Science, Hong Kong Baptist University as an Assistant Professor. He returned to the Hong Kong University of Science and Technology and is now a Professor in the Department of Computer Science and Engineering. He is serving as an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, Neural Networks, Neurocomputing, International Journal of Data Science and Analytics, Editorial Board Member of Machine Learning, Governing Board Member and Vice President for Publications of the Asia Pacific Neural Network Society (APNNS). He also served/is serving as Senior Area Chairs / Area Chairs of major machine learning / AI conferences including NeurIPS, ICML, ICLR, IJCAI, AAAI and ECML. He is recognized as the Most Influential Scholar Award Honorable Mention for "outstanding and vibrant contributions to the field of AAAI/IJCAI between 2009 and 2019".

Speech Title: Recent Advances in Automated Machine Learning
Abstract: Machine learning is powerful, but machine learning expertise are in high demand around the world. To reduce the talent gap, automated machine learning (AutoML) aims to automatically construct machine learning solutions from data. In this talk, we discuss several recent advances in the use of AutoML for effective neural network designs, for the search of functions that capture interactions among items and users in recommender systems, and for selecting samples into the training process when the machine learning application has lots of noisy samples. Extensive experiments demonstrate the effectiveness of AutoML in all these different scenarios, and AutoML can obtain much better performance than state-of-the-art approaches.





Prof. Zhongfei Zhang
IEEE Fellow

Binghamton University, State University of New York, USA

Zhongfei Zhang is a professor at Computer Science Department, Binghamton University, State University of New York (SUNY), USA. He received a B.S. in Electronics Engineering (with Honors), an M.S. in Information Sciences, both from Zhejiang University, China, and a PhD in Computer Science from the University of Massachusetts at Amherst, USA. He was on the faculty of Computer Science and Engineering at SUNY Buffalo, before he joined the faculty of Computer Science at SUNY Binghamton. He is the author or co-author of the very first monograph on multimedia data mining and the very first monograph on relational data clustering. He has published over 200 papers in the premier venues in his areas. He holds more than thirty inventions, has served as members of organization committees of several premier international conferences in his areas, and as editorial board members for several international journals. He served as a French CNRS Chair Professor of Computer Science at the University of Lille 1 in France, a JSPS Fellow at Chuo University in Japan, a QiuShi Chair Professor at Zhejiang University in China, as well as several visiting professorships at many universities and research labs in the world when he was on leave from Binghamton University years ago. He received many honors including SUNY Chancellor’s Award for Scholarship and Creative Activities, SUNY Chancellor's Promising Inventor Award, and best paper awards from several premier conferences in his areas. He is an IEEE Fellow.

Speech Title: Exploiting Strategies for Learning Effectiveness
Abstract: How to improve and enhance learning effectiveness is always a central research theme in machine learning community. In the literature, different strategies are used and exploited to boost learning effectiveness for different specific learning problems. In this talk, I will examine a couple of different problems and address how to develop and exploit new strategies in order to develop solutions to improve and enhance the learning effectiveness.