Prof. Qiang Yang

Chief Artificial Intelligence Officer (CAIO) of WeBank

Chair Professor of Computer Science and Engineering Department at HKUST

Fellow of RSC,CAE, AAAI, AAAS, IAPR, IEEE, ACM and CAAI

Trustworthy Federated Learning

Abstract: Federated Learning is an important intersection of AI and privacy computing. How to make Federated Learning more safe, trustworthy and efficient is the focus of industry and academia in the future. In my lecture, I will systematically review the progress and challenges of Federated Learning, and look forward to several important development directions.

Dr. Qiang Yang is a Fellow of Canadian Academy of Engineering (CAE) and Royal Society of Canada (RSC), Chief Artificial Intelligence Officer of WeBank and Chair Professor of CSE Department of Hong Kong Univ. of Sci. and Tech. He is the Conference Chair of AAAI-21, President of Hong Kong Society of Artificial Intelligence and Robotics(HKSAIR) , the President of Investment Technology League (ITL) and Open Islands Privacy-Computing Open-source Community, and former President of IJCAI (2017-2019). He is a fellow of AAAI, ACM, IEEE and AAAS. His research interests include transfer learning and federated learning. He is the founding EiC of two journals: IEEE Transactions on Big Data and ACM Transactions on Intelligent Systems and Technology. His latest books are Transfer Learning , Federated Learning , Privacy-preserving Computing and Practicing Federated Learning.

Prof. Huan Liu

Regents Professor of Arizona State University

Fellow of ACM, AAAI, AAAS, and IEEE

Social Media Mining: A Bountiful Frontier in AI and Data Science

Abstract: Social media data differs from conventional data in many ways. It is not only big, but also noisy, linked, multimodal, and user generated. Unprecedented opportunities thus emerge for researchers in AI and Data Science through the lens of social media data. In this talk, we use examples to illustrate (1) fundamental problems associated with social media, challenging common practice and existential understanding in machine learning and data mining, (2) intriguing questions, unique to social media, that can be answered by mining social data, and (3) how we can make a difference – that is, contributing to society at large - by developing novel AI algorithms in our work on social media mining. There are abundant opportunities for interdisciplinary collaborations in AI and data science. We further contemplate the role of social media mining in the rapid development of AI and associated research issues.

Dr. Huan Liu is a Regents Professor and Ira A. Fulton professor of Computer Science and Engineering at Arizona State University. He is the recipient of the ACM SIGKDD 2022 Innovation Award for his outstanding contributions to the foundation, principles, and applications of social media mining and feature selection for data Mining. At ASU, he was recognized for excellence in teaching and research in Computer Science and Engineering. He co-authored the textbook, Social Media Mining: An Introduction, by Cambridge University Press. He is Editor in Chief of ACM TIST, Founding Field Chief Editor of Frontiers in Big Data, its Specialty Chief Editor of Data Mining and Management, and a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction. He is a Fellow of ACM, AAAI, AAAS, and IEEE.

Prof. Cheng-Zhong Xu

Dean and Chair Professor of FST, UM

IEEE Fellow

Connected and Autonomous Driving and AIGC Opportunities

Abstract: Autonomous driving is breaking the dawn of a new era, mainly due to breakthroughs of AI technologies. This talk will provide a comprehensive review of state-of-the-art technologies in environment perception, scenario understanding, mapping and location, intelligent path planning. It will also introduce a MoCAD project for Macau Connected and Autonomous Driving, which is under development at University of Macau. It aims to develop key enabling technologies in open environments with assistance of vehicle-infrastructure networking and cloud/edge computing technologies, and to construct a first-class test and evaluation platform for autonomous driving in the greater bay area. It will present recent research results on robustness deep machine learning algorithms in open environments and transfer learning approaches for model adaptivity in corner driving scenarios. Opportunities due to AI generated contents for autonomous driving will also be discussed.

Dr. Cheng-Zhong Xu, IEEE Fellow, is the Dean of the Faculty of Science and Technology, University of Macau, Macao SAR, China and a Chair Professor of Computer Science of UM. He is also a Chief Scientist of a key project on “Internet of Things for Smart City” of Ministry of Science and Technology of China and a key project on “Intelligent Driving” of Macau SAR. He was a Chief Scientist of Shenzhen Institutes of Advanced Technology (SIAT) of Chinese Academy of Sciences and the Director of Institute of Advanced Computing and Digital Engineering. Prior to these, he was in the faculty of Wayne State University, USA for 18 years. Dr. Xu's research interest is mainly in the areas of parallel and distributed systems, cloud and edge computing, and data-driven intelligent applications. He has published over 400 peer-reviewed papers on these topics and awarded more than 120 patents. Dr. Xu was the Chair of IEEE Technical Committee of Distributed Processing. He received his B.S. and M.S. degrees in Computer Science from Nanjing University and his Ph.D. from the University of Hong Kong in 1993.

Prof. Chengqi Zhang

Distinguished Professor of University of Technology Sydney

ACS Fellow

Chengqi Zhang has been appointed as a Pro Vice-Chancellor (China Enterprise) on 1 December 2021 at the University of Technology Sydney (UTS), a Distinguished Professor on 27 February 2017 at UTS. In addition, he has been elected as the Chairman of the Australian Computer Society National Committee for Artificial Intelligence since November 2005 and he was elected as a General Chair of IJCAI-2024.

Prof. Zhang is a Fellow of the Australian Computer Society (ACS) and a Senior Member of the IEEE Computer Society (IEEE). Additionally, he served in the ARC College of Experts from 2012 to 2014. He had been elected as the founding Chair of the Steering Committee of the International Conference on Knowledge Science, Engineering, and Management between 2006 and 2014. He has been serving as an Associate Editor for three international journals, including IEEE Transactions on Knowledge and Data Engineering from 2005 to 2008; and he served as General Chair, PC Chair, or Organising Chair for five international Conferences including KDD 2015, ICDM 2010 and WI/IAT 2008. He is also the Local Arrangements Chair of IJCAI-2017 in Melbourne (International Joint Conference on Artificial Intelligence), and was appointed as IJCAI Sponsorship Officer, and General Chair of IJCAI-2024.

Prof. Qing LI

Chair Professor of HK PolyU

Fellow of IEEE and IET/IEE

Prof. Qing Li is currently a Chair Professor (Data Science) and the Head of the Department of Computing, the Hong Kong Polytechnic University. Formerly, he was the founding Director of the Multimedia software Engineering Research Centre (MERC), and a Professor at City University of Hong Kong where he worked in the Department of Computer Science from 1998 to 2018. Prior to these, he has also taught at the Hong Kong University of Science and Technology and the Australian National University (Canberra, Australia).

Prof. Li served as a consultant to Microsoft Research Asia (Beijing, China), Motorola Global Computing and Telecommunications Division (Tianjin Regional Operations Center), and the Division of Information Technology, Commonwealth Scientific and Industrial Research Organization (CSIRO) in Australia. He has been an Adjunct Professor of the University of Science and Technology of China (USTC) and the Wuhan University, and a Guest Professor of the Hunan University (Changsha, China) where he got his BEng. degree from the Department of Computer Science in 1982. He is also a Guest Professor (Software Technology) of the Zhejiang University (Hangzhou, China) -- the leading university of the Zhejiang province where he was born.

Prof. Li has been actively involved in the research community by serving as an associate editor and reviewer for technical journals, and as an organizer/co-organizer of numerous international conferences. Some recent conferences in which he is playing or has played major roles include APWeb-WAIM'18, ICDM 2018, WISE2017, ICDSC2016, DASFAA2015, U-Media2014, ER2013, RecSys2013, NDBC2012, ICMR2012, CoopIS2011, WAIM2010, DASFAA2010, APWeb-WAIM'09, ER'08, WISE'07, ICWL'06, HSI'05, WAIM'04, IDEAS'03,VLDB'02, PAKDD'01, IFIP 2.6 Working Conference on Database Semantics (DS-9), IDS'00, and WISE'00.

Prof. Lei Chen

Chair Professor of HKUST

IEEE Fellow

Dr. Lei Chen has BS degree in computer science and engineering from Tianjin University, Tianjin, China, MA degree from Asian Institute of Technology, Bangkok, Thailand, and PhD in computer science from the University of Waterloo, Canada. He is a chair professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST). Currently, Prof. Chen serves as the head of Data Science and Analytic trust at HKUST (GZ), director of Big Data Institute at HKUST, director of HKUST MOE/MSRA Information Technology Key Laboratory.

Prof. Chen’s research interests include human-powered machine learning, crowdsourcing, Blockchain, graph data analysis, probabilistic and uncertain databases and time series and multimedia databases. Prof. Chen got the SIGMOD Test-of-Time Award in 2015.The system developed by Prof. Chen’s team won the excellent demonstration award in VLDB 2014. Prof. Chen has served as VLDB 2019 PC Co-chair and Editor-in-Chief of VLDB Journal. Currently, Prof. Chen serves as Editor-in-Chief of IEEE Transaction on Data and Knowledge Engineering and PC Co-chairs of IEEE Conference on Data Engineering (ICDE 2023). He is an IEEE Fellow, ACM Distinguished Member and an executive member of the VLDB endowment.

Prof. Xu Yu

Professor of CUHK

Dr. Jeffrey Xu Yu is a Professor of the Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. His current main research interests include keywords search in relational databases, graph mining, graph query processing, and graph pattern matching. Dr. Yu served/serves in over 300 organization committees and program committees in international conferences/workshops including the PC Co-chair of APWeb’04, WAIM’06, APWeb/WAIM’07, WISE’09, PAKDD’10, DASFAA’11, ICDM’12, NDBC’13, ADMA’14, CIKM’15, Bigcomp’17, DSAA’19 and CIKM’19, and conference general co-chair of APWeb’13 and ICDM’18.

Dr. Yu served as an Information Director and a member in ACM SIGMOD executive committee (2007-2011), an associate editor of IEEE Transactions on Knowledge and Data Engineering (2004-2008), an associate editor in VLDB Journal (2007-2013), and the chair of the steering committee in Asia Pacific Web Conference (2013-2016). Currently, he serves as associate editor in ACM Transactions on Database Systems, WWW Journal, the International Journal of Cooperative Information Systems, the Journal of Information Processing, and Journal on Health Information Science and Systems.