AI for social good


Distinguished Professor  Chengqi Zhang

Associate Vice-President (Research Relationships China)

University of Technology Sydney

Chairperson of Australian Computer Society National Committee for AI

General Chair of IJCAI-2024

The Title of Speech: AI for social good

Abstract of Speech:

AI has been successfully applied to everyday lives at an accelerating pace, and it has a great potential to provide tremendous social good in the future. In this talk, Prof Chengqi Zhang will introduce a brief history of AI , recent advances in AI, and the recent work that are using the forefront AI for social good, such as health, logistics, and agriculture. He will then talk about the potential use of AI in various topics that are essential for social good in the future. The new challenges raised around AI and ethics, AI and Humans will be discussed at the end.

Biography of the Speaker:

Distinguished Prof Chengqi Zhang is the Associate Vice President (Research Relationships China) at the University of Technology Sydney (UTS). He has been the Chairman of the Australian Computer Society National Committee for Artificial Intelligence since November 2005 till now.

He is a worldwide recoginised research leader in the areas of data mining and machine learning. So far, a total of 342 papers have been published. He was invited to give 20 keynote speeches at international conferences. Thirty PhD students were instructed to complete their doctoral studies, and eight of them are now full professors. In 2011, he received the NSW Science and Engineering (Engineering and ICT) award and the award for UTS Vice Chancellor for Excellence in Research (Leadership). He served in the ARC College of Experts from 2012 to 2014.  Additionally, he had served as General Chair for three world top conferences, ICDM 2010, KDD 2015, and IJCAI 2024.

Dr. Guojie Song: Network Representation Learning

Abstract: Feature engineering is one of the most important parts of machine learning task. As a most universal format of data existence, such as social network, information network etc, information networks have been widely studied within a broad scope of machine learning. Network Representation Learning (NRL) aims to project network nodes (or other entities) into a low-dimensional vector space while preserving general proximity between nodes, and has become a hot topic in data mining and machine learning. In this talk, I will first overview the basic concepts of NRL, and give an introduction about our recent advancements in such field, such as tag embedding, domain adaptive network embedding, and landmark based network embedding etc.

BiographyGuojie Song is an associate professor in the School of Electronics Engineering and Computer Science at Peking University. His research interests mainly include data mining and machine learning on network data, such as social network analysis, transportation network analysis etc. He has published 100+ papers, and more than 50+ paper published at leading journals and conferences, including TKDE, TPDS, TITS, TKDD, AAAI, IJCAI, CIKM, ICDM etc. He served as PC member and Senior PC member of AI related top conferences. His research results have received three awards from ministry and provinces in China, such as the first prize of Science and Technology award of China Highway Society (2012, 2013) and the Second Prize of Shanxi Science and Technology Award (2012).

Dr. Yong Xiang: Privacy Protection in Practical Applications

Presenter: Yong Xiang


Privacy protection is an emerging and important issue in modern information era. In this talk, I will first introduce the concept and background of privacy protection. Then I will outline the potential research themes in the area of privacy protection. After that, more details about these research themes will be presented in relation to some practical applications, including privacy protection in biometric authentication systems, Internet of Things, and big data. Current challenges and future research will also be discussed along these research themes. 


Yong Xiang is a Professor at the School of Information Technology, Deakin University, Australia. He is also the Director of Deakin-Southwest University (SWU) Joint Research Centre on Big Data, the Director of Data to Intelligence Research Centre, and the Director of Deakin Blockchain Innovation Lab. He was the Associate Head of School (Research) (2013-2018) and the Director of Artificial Intelligence and Data Analytics Research Cluster (2013-2019). He has obtained many research grants, including 5 Discovery and Linkage grants from the Australian Research Council (ARC). He has published 5 monographs, over 130 refereed journal articles (mainly in IEEE journals), and numerous conference papers. Some of his research results have been commercialised. His current research interests include information security and privacy, signal and image processing, data analytics and machine intelligence, Internet of Things, and blockchain. Professor Xiang was a member of the ARC Research Evaluation Committee (Mathematics, Information and Computing Sciences), evaluating the Excellence in Research for Australia (ERA) in 2018. He is a Senior Area Editor of IEEE Signal Processing Letters and an Associate Editor of IEEE Access. He was an Associate Editor of IEEE Signal Processing Letters. He served as Guest Editor for several journals, such as IEEE Transactions on Industrial Informatics, IEEE Multimedia Magazine, etc. He has been invited to give keynote speeches and chair committees in many international conferences, review papers for numerous international journals and conferences, chair technical sessions in conferences, and assess grant applications for the ARC. Professor Xiang is a senior member of the IEEE.

Dr. Senzhang Wang: AI Empowered Spatio-Temporal Data Mining for Smart City: Challenges, Solutions, and Applications


With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data generated in urban areas has become increasingly available nowadays. Mining valuable knowledge from spatio-temporal data is critically important for better design of smart city applications, such as human mobility monitoring, smart transportation, urban planning, public safety, and environmental management. As the number, volume and resolution of spatio-temporal datasets increase rapidly, traditional data mining methods, especially statistics-based methods for dealing with such data are becoming overwhelmed. Recently, with the great success of AI techniques, especially deep learning methods, various AI techniques have been widely applied in analyzing rich spatio-temporal data generated in urban areas. In this tutorial, I will present our recent research on AI empowered spatio-temporal data mining. First, I will introduce the new challenges and opportunities of applying AI techniques to address various spatio-temporal data mining tasks in general. Then I will summarize a general framework to show the pipeline of AI empowered spatio-temporal data mining, followed by several examples illustrating how AI techniques including both shallow models and deep learning models can be utilized in the applications of demand-supply prediction in on-demand services, urban traffic prediction and urban crowd flow prediction. Finally, I will conclude the limitations of current research and point out future research directions.

About the speaker:

Dr. Senzhang Wang is currently an associate professor at College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, and also a “Hong Kong Scholar” Postdoc Fellow at Department of Computing, The Hong Kong Polytechnic University. His main research focus is on spatio-temporal data mining, social computing, graph mining and smart city. He has published more than 60 papers in premier conferences and journals of related areas such as KDD, TKDE, ICDM, SDM, AAAI, IJCAI, CIKM, TOIS, KAIS, etc.