The Workshop on Large Language Models for Multimodal Data Fusion (LLM4MDF) is a specialized, in-depth forum collocated with the IEEE International Conference on Data Mining (ICDM). It targets the emerging intersection of large language models (LLMs), multimodal learning, and data fusion, which are high-impact directions aligned with the core mission to advance data mining theory, algorithms, and applications. LLM4MDF focuses on LLM-driven fusion of text, images, audio, video, time series, graphs, and sensor data, bridging semantic understanding and heterogeneous data integration.
This workshop directly extends the scope of ICDM on multimodal data mining, heterogeneous data integration, and large-model-driven knowledge discovery. It complements the main conference by fostering cross-community collaboration on LLM-powered multimodal fusion, addressing critical challenges in unifying diverse data types for reliable, interpretable, and scalable data mining. By bringing together researchers and practitioners, LLM4MDF drives state-of-the-art advances in multimodal data mining and supports the goal of shaping the future of data science.
Please follow the submission guideline from the ICDM 2026 Submission Website.
Weiwei Jiang, Beijing University of Posts and Telecommunications, China (jww@bupt.edu.cn)
Prof. Weiwei Jiang (IEEE Senior Member, ACM Member) is currently an associate professor with the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, and Key Laboratory of Universal Wireless Communications, Ministry of Education. He received the B.Sc. and Ph.D. degrees from the Department of Electronic Engineering, Tsinghua University, Beijing, China, in 2013 and 2018, respectively. His current research interests include artificial intelligence, machine learning, big data, wireless communication and edge computing. He has published more than 100 academic papers in IEEE Trans and other journals, with more than 6400 citations in Google Scholar. He is one of 2022, 2023, 2024 and 2025 Stanford's List of World's Top 2% Scientists.
Stefano Cirillo, University of Salerno, Italy (scirillo@unisa.it)
Prof. Stefano Cirillo is an Assistant Professor (Tenure Track) in the Data Science and Artificial Intelligence Systems group within the Department of Computer Science at the University of Salerno, Italy. His research interests include Artificial Intelligence, Data Profiling, Data Privacy, and Social Networks. He was a Visiting Researcher at the Hasso Plattner Institute in the Information Systems Group. He is an Associate Editor of several journals and has published extensively in top-tier venues.
Ahmad Taher Azar, Prince Sultan University, Saudi Arabia (aazar@psu.edu.sa)
Prof. Ahmad Taher Azar is a full Professor at Prince Sultan University, Riyadh, Kingdom Saudi Arabia. He is the leader of Automated Systems & Computing Lab (ASCL), Prince Sultan University, Saudi Arabia. Prof. Azar specializes in artificial intelligence, machine learning, control theory and applications, robotics, computational intelligence, reinforcement learning, and dynamic system modeling. He has published/co-published over 500 research papers, book chapters, and conference proceedings in prestigious peer-reviewed journals.
Muhammet Deveci, University College London, UK (m.deveci@ucl.ac.uk)
Dr. Muhammet Deveci is currently an Honorary Senior Research Fellow at The Bartlett School of Sustainable Construction in the University College London, London, UK. He is also a Full Professor at the Department of Industrial Engineering in the Turkish Naval Academy, National Defence University, Istanbul, Turkey. Dr Deveci is an outstanding researcher and a prolific author with over 330 papers in journals indexed by SCI/SCI-E. His research focuses on intelligent decision support systems underpinned by computational intelligence.