Web Intelligence meets Brain Informatics (WImeetsBI)

Co-located with The 24th IEEE/WIC IIEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2025)

Co-located with The 18th International Conference on Brain Informatics (BI 2025)

We warmly invite researchers and enthusiasts from diverse fields, including data science, network science, service science, intelligent agents, human-centric computing, brain-inspired computing, brain-machine intelligence, artificial intelligence, embodied intelligence, machine learning, deep learning, knowledge management, information retrieval, digital twins, FAccT, LLMs and AIGC, to join the WImeetsBI 2025 workshop.

Web Intelligence and Brain Informatics are committed to conducting in - depth fusion research. From the perspective of Web Intelligence, the research focuses on both deepening the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enabling the development and application of intelligent computing technologies. To achieve its goals, Web Intelligence employs cutting - edge technologies such as data mining, machine learning, knowledge graph, intelligent agents, and natural language processing to analyze web data and create intelligent tools and services that offer personalized and valuable insights to users.

On the other hand, Brain Informatics brings a unique dimension to the research. It delves into the information processing mechanisms underlying human cognition, perception, and decision - making processes. Brain Informatics requires new research paradigms, computational methods, and tools from Web Intelligence. By integrating knowledge about how the brain processes information, Brain Informatics can develop more human - centric intelligent computing models. This, in turn, enables the development and application of intelligent computing technologies that are not only technically advanced but also closely aligned with human cognitive abilities and behavioral patterns, thereby creating more intuitive and effective web - based systems and services.

Topics of Interest

Our research interests encompass a wide range of topics, including but not limited to:

  • Complex brain science investigations
  • BI research driven by WI needs
  • Innovative technologies for brain science
  • Collective intelligence at the human level
  • Interactions of brain big data in a social-cyber-physical space
  • Computing and services for brain big data
  • Brain-machine intelligence and brain-inspired computing
  • Text mining applications in brain informatics
  • Knowledge graph construction for brain informatics
  • BI research on cognition, emotion, and disease
  • Intelligent health technologies
  • Multi-task and multimodal data analysis
  • Fusion computing of multi-source data and knowledge

Organizers

Jianzhuo Yan

Beijing University of Technology, China

Hongzhi Kuai

Chongqing University of Posts and Telecommunications, China

Jianhui Chen

Beijing University of Technology, China

Jiajin Huang

Beijing University of Technology, China

Workshop Schedule in conjunction with Brain Informatics 2025

Part One

November 12, 2025 / Beijing, China

17:00-17:30 (BJT) 10:00-10:30 (CET)

Opening Address

Ning Zhong, Maebashi Institute of Technology, Japan

Invited Talk: 17:30-18:00 (BJT) 10:30-11:00 (CET)

Towards Inclusive AI for Improved Brain Disorder Detection and Management: Challenges and Opportunities

Mufti Mahmud, King Fahd University of Petroleum and Minerals, Saudi Arabia

Invited Talk: 18:00-18:30 (BJT) 11:00-11:30 (CET)

Machine Learning Integrating Disease Mechanisms for Population Health Analysis

Hong Yu, Chongqing University of Posts and Telecommunications, China

Invited Talk: 18:30-19:00 (BJT) 11:30-12:00 (CET)

Systematic Brain Computing for Understanding of High-Order Cognition

Hongzhi Kuai, Chongqing University of Posts and Telecommunications, China

Paper Presentation: 19:00-19:15 (BJT) 12:00-12:15 (CET)

An Interpretable Framework based on Knowledge Distillation for Hypertension Early Warning Model

Yumiao Chang, Shaofu Lin, Jianhui Chen, Beijing University of Technology, China

Paper Presentation: 19:15-19:30 (BJT) 12:15-12:30 (CET)

A Transformer-Based Model for Renal Cell Carcinoma Prediction

Jiatong Fan, Zitong Zhang, Jianhui Chen, Beijing University of Technology, China

Paper Presentation: 19:30-19:45 (BJT) 12:30-12:45 (CET)

Exploration of chronic disease pre-triage system based on LLM and RGA

Yu Zheng, Rui Han, Hongxia Xu, Beijing University of Technology, China

Xuerui Cheng, University of Illinois Urbana-Champaign, USA

Paper Presentation: 19:45-20:00 (BJT) 12:45-13:00 (CET)

A Graph-Aware Transformer Model for Event Extraction in Hotline Texts

Zining Luo, Zhiyi Tang, Jianhui Chen, Beijing University of Technology, China

Workshop Schedule in conjunction with WI-IAT 2025

Part Two

November 17, 2025 / Beijing , China

Invited Talk: 17:00-17:30 (BJT) 9:00-9:30 (GMT)

Advancing Mind-Inspired Multimodal and Web Intelligence

Jerry Jialie Shen, City St George's, University of London, UK

Invited Talk: 17:30-18:00 (BJT) 9:30-10:00 (GMT)

Harnessing AI Models’Uncertainty for Decision Making

Xi Niu, University of North Carolina at Charlotte, USA

Paper Presentation: 18:00-18:20 (BJT) 10:00-10:20 (GMT)

Boosting Relation Extraction with Auxiliary Tasks and Dependency Graphs

Ruiqi Sheng, Lei Liu, Jing Li, Beijing University of Technology, China

Xinyu Cao, Haitao Wang, Fundamental Standardization China National Institute of Standardization, China

Shuying Yan, Biaoxin Science & Technology (Beijing) Co,.Ltd., China

Paper Presentation: 18:20-18:40 (BJT) 10:20-10:40 (GMT)

Dynamic Memory-Aware Causal Subgraph Learning for Irregular Clinical Time Series

Jinquan Ji, Yu Cao, Chunyi Hou, Jianzhuo Yan, Beijing University of Technology, China

Paper Presentation: 18:40-19:00 (BJT) 10:40-11:00 (GMT)

TEMPO-mRS: An Explainable Multi-task Ordinal Deep Learning Model for Predicting 30-, 60-, and 90-Day mRS After Intravenous Thrombolysis in a Chinese Cohort

Lingli Li, Jianzhuo Yan, Beijing University of Technology, China

Hongxiao Li, Heilongjiang Communications Polytechnic Harbin, China

Chen Zhou, Beijing Institute for Brain Disorders Capital Medical University, China

Paper Presentation: 19:00-19:20 (BJT) 11:00-11:20 (GMT)

Multivariate Time Series Forecasting Model with Adaptive Multi-scale Hierarchical Information Interaction

Panpan Qiu, Jianzhuo Yan, Hongxia Xu, Yongchuan Yu, Beijing University of Technology, China

Tian Qin, University of Sydney Sydney, Australia