The Special Session on Intelligent Drive Data: The Fusion Revolution of Artificial Intelligence and Big Data aims to gather researchers and practitioners from academia and industry to explore how data-centric artificial intelligence is reshaping intelligent systems and decision-making. As data volumes, modalities, and acquisition speeds continue to grow explosively, the tight integration of AI algorithms with large-scale, high-quality data has become the core driving force behind breakthroughs in perception, prediction, optimization, and autonomy. This session focuses on how to transform raw, heterogeneous data into actionable intelligence that can effectively drive complex systems in dynamic and uncertain environments.
The session seeks contributions that span foundational theories, key technologies, and real-world applications of AI–big data fusion. Topics include intelligent data acquisition and governance, scalable learning architectures, multimodal data fusion, real-time analytics for autonomous systems, and trustworthy, secure, and interpretable AI in data-intensive scenarios. Application domains of interest range from autonomous driving, intelligent transportation and logistics, smart manufacturing and industrial IoT, to healthcare, finance, and urban computing. By bridging data engineering, machine learning, and domain expertise, this special session aims to advance the next generation of “intelligent drive data” paradigms and promote cross-disciplinary collaboration.
“智驱数据：人工智能与大数据的融合革命”专题论坛旨在汇聚学术界与工业界的研究人员和实践者，系统探讨以数据为驱动力的人工智能如何重塑智能系统与决策模式。随着数据规模的指数级增长以及多模态感知与采集技术的快速发展，人工智能算法与大数据资源的深度融合，正成为智能感知、预测分析、优化控制与自主决策等能力跃迁的关键。本专题将围绕如何将海量、异构、非结构化的数据高效转化为可解释、可落地、可迭代演化的“智驱数据”展开，聚焦智能数据采集与治理、可扩展学习架构、多模态数据融合、面向自主系统的实时分析，以及面向高风险场景的可信、安全与隐私保护等前沿问题。论坛也欢迎在自动驾驶与智能交通、工业互联网与智能制造、智慧医疗与金融科技、智慧城市与时空大数据等典型应用领域的最新研究与实践成果，以期推动人工智能与大数据的深度耦合和多学科交叉创新，驱动新一轮数据智能革命。
Chair: Assoc. Prof. Ziyu Jia, Chinese Academy of Sciences, China
Ziyu Jia is an Associate Professor at the Institute of Automation, Chinese Academy of Sciences. His research focuses on time-series analysis methods and their applications in health and medicine, including multimodal affective computing, sleep stage classification, and brain-computer interfaces. He has published over 50 peer-reviewed papers in venues such as IEEE Transactions on Affective Computing, IEEE Transactions on Multimedia, IEEE Transactions on Neural Systems and Rehabilitation Engineering, KDD, and ICLR. Dr. Jia currently serves as an Associate Editor or Editorial Board Member for prestigious journals including IEEE Transactions on Affective Computing and Information Fusion, and he is an Area Chair for major AI and machine learning conferences such as IJCAI and IJCNN. In addition to his academic contributions, Dr. Jia has extensive industry experience, having successfully led multiple R\&D projects and secured several patents. He has received numerous honors, including the MSRA StarTrack Award, and the CIE Young Talent Award.
Co-chair: Mr. Chenyu Liu, Nanyang Technological University, Singapore
Chenyu Liu received his B.E. degree in software engineering from the University of Electronic Science and Technology of China, Sichuan, China, in 2020. He is working toward his Ph.D. degree in the College of Computing and Data Science, Nanyang Technological University, Singapore, 639798, Singapore. His research has been published in top-tier conferences, such as the International Conference on Learning Representations and the International Joint Conference on Artificial Intelligence. His research interests include brain–computer interfaces, multivariate time series, and multimodal spatiotemporal data mining.
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