ICCBDCS Keynote Speaker
Yu-Wang Chen, University of Manchester, United Kingdom
Biography: Yu-Wang Chen is Professor of Decision Sciences and Business Analytics at Alliance Manchester Business School (AMBS), The University of Manchester. He was Turing Fellow at The Alan Turing Institute from 2021 to 2023. Prior to joining AMBS, he worked briefly as a Postdoctoral Research Fellow at the Department of Computer Science, Hong Kong Baptist University. He received the PhD degree in System Engineering from Shanghai Jiao Tong University.
His research focuses on Decision Sciences, Data Analytics and AI, with applications in business analytics, maritime transportation, risk analysis, healthcare decision support, etc. He has published more than 100 research articles in leading journals, such as EJOR, IEEE SMC, IS, IF, KBS, and C&OR, 5 books or book chapters and 20+ publications in conference proceedings.
He has been awarded a portfolio of research or industry projects as PI, Joint PI or Co-I by Innovate UK, Turing-UoM Fund, EPSRC, ERDF, etc. He currently serves as Associate Editor and Editorial Board Member of multiple international journals.
Title of Speech: Data-Driven Decision Making in Social Networks
Abstract: Data-driven decision-making is widely recognized as fundamental to enhancing decision-making process and outcomes in business and management. In this presentation, I will briefly discuss our research on developing a holistic framework for modelling and analyzing decision making and behaviors in social communities. This framework explores how individual decision-makers implicitly form their initial beliefs within the paradigm of multiple criteria decision making, interact with others, and exchange beliefs, leading to dynamic belief updating. Ultimately, these interactions shape group opinion dynamics and influence decision making process and outcomes within the social network environment. The framework is expected to provide valuable insights into decision making and behaviors in social networks and offer guidance for future research and real-world applications that bridge decision sciences with social network analysis.
Tianrui Li, Southwest Jiaotong University, China
Biography: Dr Tianrui Li is a Professor, Dean of School of Computing and Artificial Intelligenc, and the Director of the Key Lab of Cloud Computing and Intelligent Technique of Sichuan Province, Southwest Jiaotong University, China. Since 2000, he has co-edited 10 books, 12 special issues of international journals, received 36 Chinese invention patents and published over 500 research papers with more than 26,000 citations in refereed journals (e.g., AI, IJCV, IEEE TPAMI, IEEE TKDE, IEEE TIFS, IEEE TPDS) and conferences (e.g., AAAI, ACL, CVPR, ICCV, ICDE, ICML, IJCAI, KDD, UbiComp, WWW). 7 papers were ESI Hot Papers and 26 papers were ESI Highly Cited Papers. He has been ranked among the world’s top 2% scientists and honored as a China Highly Cited Researcher. He serves as Editor-in-Chief of Human-Centric Intelligent Systems, Area Editor of International Journal of Computational Intelligence Systems, Editor of Information Fusion, Associate Editor of ACM Transactions on Intelligent Systems and Technology, etc. He is the Fellow and Vice-President of IRSS, and the Secretary of ACM SIGKDD China Chapter.
Title of Speech: Hierarchical Learning for 3D Visual Intelligence
Abstract: Human cognition naturally organizes visual information in hierarchical and multi-granular forms, providing inspiration for advancing 3D visual intelligence. To bridge the gap between semantic hierarchies and spatial multi-scale structures in visual understanding, this report systematically introduces a novel framework of ‘Hierarchical Learning’. The framework integrates three components: semantic modeling guided by cognitive semantic hierarchies, representation mechanisms driven by geometrical hierarchical structural priors, and perceptual stability ensured by hierarchical robust sampling - together forming a new framework for 3D visual understanding that aligns more closely with human multi-granular cognition. We are the first to establish a theoretical link between semantic consistency across hierarchical semantic layers and entropy maximization within each layer. Based on it, a novel Hierarchical Embedding Fusion Module (HEFM) and a hierarchical regularization term are proposed. To support datasets without pre-defined hierarchies, we propose a new label class sematic hierarchy generation algorithm based on vision-language models and design a quality evaluation metric. Finally, within this framework, to address real-world 3D point cloud corruptions, we also introduce a robust local-global balanced point cloud sampling protocol. Experimental results show that, compared to state-of-the-art methods, our approach significantly improves the performance of 3D semantic segmentation and robust classification, demonstrating strong potential in applications such as autonomous driving, urban planning, and digital twins.