Overview
Large language models (LLMs), agentic AI systems, and sophisticated data generation techniques are fundamentally re-shaping traditional data mining paradigms that have long relied on structured extraction, pattern discovery, and supervised learning from static or streaming datasets. This panel explores how generative models facilitate high-quality synthetic data creation and open-world adaptation, how agentic frameworks introduce autonomous reasoning, exploration, and iterative discovery into mining pipelines, and how these advances challenge core assumptions around data scarcity, label quality, scalability, privacy, and interpretability.
Schedule
Moderators
Min-Ling Zhang
Professor
Southeast University
Research interests: artificial intelligence, machine learning and data mining
Raymond Chi-Wing Wong
Professor
The Hong Kong University of Science and Technology
Research interests: database and data mining
Panelists
Philip S. Yu
Distinguished Professor
University of Illinois Chicago
Research interests: big data, data mining (especially on graph/network mining), social network, privacy preserving data publishing, data stream, database systems, and Internet applications and technologies
Christian S. Jensen
Professor
Aalborg University
Research interests: data analytics and management, and much of my research concerns foundations for value creation from temporal, time-series, spatial, spatio-temporal, geo-textual, and multidimensional data, including data modeling and database design, data models and query languages, query and update processing, data mining, machine learning, and indexing.
My T. Thai
UF Research Foundation Professor of Computer & Information Sciences & Engineering, Associate Director of UF Nelms Institute for the Connected World
University of Florida
Research interests: trustworthy AI, autonomous intelligent system, optimization
Francesco Piccialli
Full Professor of Computer Science
Department of Mathematics and Applications “R. Caccioppoli”, University of Naples Federico II, Italy
Research interests: Federated and Distributed Learning, Edge AI, Green and Sustainable AI, Generative and Agentic AI, Digital Twins, Scientific Machine Learning, Trustworthy AI, Time-Series Analysis and Predictive Intelligence
Ling Chen
Professor
University of Technology Sydney (UTS)
Research interests: artificial intelligence, information systems, machine learning
