Keynote Speakers

Professor and Vice President, Nanjing University

Zhi-Hua Zhou is Professor of Computer Science and Artificial Intelligence, Vice President of Nanjing University. His research interests are mainly in machine learning and data mining, with significant contributions to ensemble learning, multi-label and weakly supervised learning, etc. He has authored the books "Ensemble Methods: Foundations and Algorithms", "Machine Learning", etc., and published more than 200 papers in top-tier journals or conferences, with more than 100,000 citations according to Google Scholar. Many of his inventions have been successfully deployed in industry. He is President of IJCAI Trustee, Series Editor of Springer Lecture Notes in Artificial Intelligence, Editor-in-Chief of Frontiers of Computer Science, and advisory board member of AI Magazine. He founded ACML (Asian Conference on Machine Learning). He served for PAKDD as Program Chair (2007, 2015), General Chair (2014, 2019), and Steering Committee life member. He is Fellow of the ACM, AAAI, AAAS, IEEE, etc., and recipient of the National Natural Science Award of China, the IEEE Computer Society Edward J. McCluskey Technical Achievement Award, the CCF-ACM Artificial Intelligence Award, PAKDD Distinguished Contribution Award, etc.

Professor, Monash University

Professor Geoff Webb is an eminent and highly-cited AI researcher. He is an Australian Research Council Laureate Fellow and Professor in the Monash University Department of Data Science and Artificial Intelligence. He was editor in chief of the Data Mining and Knowledge Discovery journal, from 2005 to 2014. He has been Program Committee Chair of both ACM SIGKDD and IEEE ICDM, as well as General Chair of ICDM and member of the ACM SIGKDD Executive. He is a Technical Advisor to machine learning as a service startup BigML Inc and to recommender systems startup FROOMLE. He developed many of the key mechanisms of support-confidence association discovery in the 1980s. His OPUS search algorithm remains the state-of-the-art in rule search. He pioneered multiple research areas as diverse as black-box user modelling, interactive data analytics and statistically-sound pattern discovery. He has developed many useful machine learning algorithms that are widely deployed.  His many awards include IEEE Fellow, the inaugural Eureka Prize for Excellence in Data Science (2017), the IEEE International Conference on Data Mining Research Contributions Award, the Pacific-Asia Conference on Knowledge Discovery and Data Mining Distinguished Research Contributions Award (2022) and the IEEE International Conference on Data Mining 10-year Highest Impact Award (2023). He has thrice been recognised by The Australian Research Magazine as Australia's leading Bioinformatics and Computational Biology researcher.

Principal Scientist, Meta Reality Labs

Xin Luna Dong is a Principal Scientist at Meta Wearables AI, where she leads the Agentic AI efforts for building trustworthy and personalized assistants on wearable devices. Previously, she spent over a decade advancing knowledge graph technology, including the Amazon Product Graph and the Google Knowledge Graph. She is co-author of Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases and Big Data Integration. She is an ACM Fellow and IEEE Fellow, recognized for “significant contributions to knowledge graph construction and data integration.” She was named an ACM Fellow and an IEEE Fellow for "significant contributions to knowledge graph construction and data integration", awarded the VLDB Women in Database Research Award and VLDB Early Career Research Contribution Award, and invited as an ACM Distinguished Speaker. She serves in the PVLDB advisory committee, was a member of the VLDB endowment, a PC co-chair for KDD’2022 ADS track, WSDM’2022, VLDB’2021, and Sigmod’2018.