Hussein Abbass is a full professor with the School of Systems and Computing, University of New South Wales, Canberra. He is a Fellow of the Institute of Electrical and Electronics Engineering (IEEE) USA, a Fellow of the Australian Computer Society, a Fellow of the UK Operational Research Society, a Fellow of the Australian Institute of Managers and Leaders, and a Graduate Member of the Australian Institute of Company Directors. Hussein was the National President (2016-2019) for the Australian Society for Operations Research, the Vice-President for Technical Activities (2016-2019) for the IEEE Computational Intelligence Society, and an ExCom and AdCom member (2016-2019) of the IEEE Computational Intelligence Society. Hussein is a Distinguished Lecturer for the IEEE Computational Intelligence Society and the Founding Editor-in-Chief of the IEEE Transactions on Artificial Intelligence. Hussein is the chair of the IEEE Conference on AI Steering Committee, the incoming chair of the IEEE Frank Rosenblatt Award committee (equivalent to the technical medal in computational intelligence) and is the vice-chair for the Working Group on the IEEE P7018 Standard for Security and Trustworthiness Requirements in Generative Pretrained Artificial Intelligence (AI) Models. Hussein is a UAV pilot and a mental health first-aid officer and has completed various executive professional development training. Following ten years in industry and academia, in 2000, he joined the University of New South Wales campus in Canberra (UNSW-Canberra) at the Australian Defence Force Academy. He has been a full professor since 2007 and has served in various university leadership roles. His current research focuses on trusted quantum-enabled human-AI-swarm teaming systems and distributed and trusted machine learning and machine education systems and algorithms.

Lei Chen, is a chair professor in the data science and analytic thrust at HKUST (GZ), Fellow of the IEEE, and a Distinguished Member of the ACM. Currently, Prof. Chen serves as the dean of information hub, the director of Big Data Institute at HKUST (GZ), the director of Guangzhou Municipality Lab of Big Data Intelligence. Prof. Chen’s research interests include Data-driven AI, knowledge graphs, blockchains, data privacy, crowdsourcing, spatial and temporal databases and query optimization on large graphs and probabilistic databases. He received his 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. Prof. Chen received the SIGMOD Test-of-Time Award in 2015, Best research paper award in VLDB 2022, .The system developed by Prof. Chen’s team won the excellent demonstration award in VLDB 2014. Prof. Chen had served as VLDB 2019 PC Co-chair and an executive member of the VLDB endowment . Currently, Prof. Chen serves as general co-chairs of VLDB 2024 and Editor-in-chief of IEEE Transaction on Data and Knowledge Engineering.

Dr Ling Chen is a Professor in the School of Computer Science at UTS, Sydney, Australia. She received her PhD in Computer Engineering from Nanyang Technological University (NTU), Singapore, and undertook postdoctoral training at Leibniz University Hannover (L3S Research Centre), Germany. As the Deputy Head of School (Research) for the School of Computer Science, Ling is leading major research & development activities across different disciplines and research institutes/centres within the school. Ling also leads the Data Science and Knowledge Discovery Laboratory (The DSKD Lab) within the Australian Artificial Intelligence Institute (AAII) at UTS. Ling’s research interests mainly include (i) discovering regularities (e.g., patterns) and irregularities (e.g., outliers or novelties) from various types of data (e.g., structured/unstructured data, single-modal/multimodal data, and static/dynamic data etc.); (ii) data representation learning, including both hash-based and learning-based methods for graph structured data and spatio-temporal data; (iii) social media and social network mining, including event detection, information diffusion modelling and user profiling for recommendation; (iv) dialogue and interactive systems, including reinforcement learning (for POMDP) and continual learning. Ling has secured multiple competitive research grants, including ARC DP/LP/LIEF. Ling’s research has also been recognised and funded by the industry, including Facebook and TPG Telecom. Ling is an Editorial Board member for the IEEE Journal of Social Computing, and the Elsevier Journal of Data and Knowledge Engineering.

Prof. Amin Beheshti is a Full Professor of Data Science at Macquarie University, and Adjunct Professor of Computer Science at UNSW Sydney, Australia. Amin is the founder and director of the Centre for Applied Artificial Intelligence, the head of the Data Science Lab, and the founder of the Big Data Society at Macquarie University, Sydney, Australia. Amin completed his PhD and Postdoc in Computer Science and Engineering at UNSW Sydney, and holds a Master's and Bachelor's degree in Computer Science, both with First Class Honours. Before starting his PhD in 2009, Amin had over a decade of industry experience as a founder and CEO, consultant, and Solution Architect in national and international organizations. Alongside his teaching activities, Amin has made significant contributions to research projects and successfully secured 40+ research projects (Over $31 million in Research Funding). Amin received prestigious awards, including Excellence Award (Macquarie University, 2023), Excellence in Research Innovation, Partnership Entrepreneurship (Macquarie University, 2022), National Security Impact Award (D2D CRC, 2016 and 2017), Recognition Award (D2D CRC, 2016 and 2017), Australian Postgraduate Award (APA 2009-2012), and several Best Paper awards. In 2021, due to his outstanding performance, Amin was promoted from Senior Lecturer to Full Professor at Macquarie University. As a distinguished researcher in Data and AI Science, Amin has been invited to serve as a Keynote Speaker, General-Chair, PC-Chair, Organisation-Chair, and program committee member of top international conferences. He is also a leading author of several authored books in data, social, and process analytics, co-authored with other high-profile researchers.

Prof. Zhifeng Bao co-directs the RMIT Center of Information Discovery and Data Analytics and lead the Big Data and Database Group in the Center. He is also an Honorary Senior Fellow at The University of Melbourne. He obtained his PhD in computer science from National University of Singapore and was the winner of the Best PhD Thesis Award. He is generally interested in big data management and mining. Currently, He is working on learned indexes, query optimization in database systems, data quality for data-efficient or effective ML, data/query pricing, and ML-enhanced algorithms. He is honored to receive several research awards, such as the Chris Wallace Award for Outstanding Research by the Computing Research and Education Association of Australasia (CORE) in 2021, the Google Faculty Research Awards, the Best Paper Award Runner-up at KDD’19, and the Best Paper Award Nomination at KDD’18. He is the PC Co-chair of full paper track at CIKM’24 and ADC’23. He has been the Associate Editor of PVLDB’21, SIGMOD’23 and PVLDB’23 and had the honor to be recognized with the Distinguished Associate Editor Award for each of them. He has served the Senior TPC of flagship conferences in the broad field of data science such as KDD and WSDM. He has been a committee member in the conference ranking process for CORE 2023. He has regularly provided consultancy services to both industrial enterprises and governmental offices, such as City of Melbourne on its Smart City Project and Victoria Department of Human Health and Services on its data quality project.