This workshop tackles the challenge of transforming massive data into efficient representations through cutting-edge research in data representation and learning. We explore techniques that preserve semantic richness while optimizing computational efficiency, enabling faster processing with minimal resources. By reimagining the data-to-knowledge pipeline, we aim to build scalable, practical big data and AI systems. Join us to pioneer the next generation of intelligent systems that balance performance with sustainability.

This workshop provides a unique forum for in-depth discussion. We welcome submissions that are aligned with the workshop's thematic.

There are two ways to submit a paper: one option is to submit only the paper’s title and abstract; the other is to submit a regular or short paper, with regular papers limited to 8 pages and short papers to 4 pages (original manuscripts must be formatted as PDF files following the IEEE two-column conference format). Papers accepted under the second submission option will be published in the conference proceedings of IEEE Xplore. This workshop adopts a single-blind review policy.

You may submit your paper by emailing the organizer at: xuezhe@bupt.edu.cn

Hongzhi Yin (The University of Queensland, Australia)

Yuankai Qi (Macqurie University, Australia)

Yongxin Tong (Beihang University, China)

Zhonghong Ou (Beijing University of Posts and Telecommunications, China)

Zhongying Zhao (Shandong University of Science and Technology, China)

Wanqi Yang (Nanjing Normal University, China)

Hao Wang (University of Science and Technology of China, China)

Wenbin Li (Nanjing University, China)

Lingling Zi (Chongqing Normal University, China)

Suguo Zhu (Hangzhou Dianzi University, China)