Human-Machine-Object Fusion Heterogeneous Intelligent Agent Collaborative Computing
August 23 (Saturday) 3:45 PM-5:30 PM
Grand Ballroom A, 3rd Floor

Guest Profile

Xiao Nong

National University of Defense Technology/Sun Yat-sen University

Introduction:
        PhD, Professor and Doctoral Supervisor at the Supercomputing Center of National University of Defense Technology/Sun Yat-sen University, National High-Level Talent, CCF Fellow, and Director of the CCF Information Storage Committee. His research focuses on high-performance network computing, novel storage, and system architecture. He is a pioneer in high-performance network computing technology research in my country. He has received two second-class National Science and Technology Progress Awards, seven provincial and ministerial-level special prizes, and seven first and second-class awards. He has published over 200 papers.

 

Report Title: High-Performance Storage Technology for Multi-Computing Resources in the Computing Network

 

Report Summary: 

       The computing network's resources include various types, such as supercomputing and artificial intelligence. This report will discuss the storage issues faced by the integrated development of high-performance computing and AI in the computing network.

Li Keqiu

 Tianjin University

Introduction:
      Director, Professor, and Doctoral Supervisor of the Department of Intelligence and Computing at Tianjin University. He is a recipient of the National Outstanding Youth Fund, a leading talent in scientific and technological innovation under the National "Thousand Talents Plan," an IEEE Fellow, and the chief scientist of a key R&D project. He serves as the director of the Tianjin Key Laboratory of Advanced Network Technology and Applications. He has published over 200 academic papers, including nearly 100 in internationally renowned journals and conferences, including TON, TMC, TPDS, INFOCOM, MobiCom, ATC, and ASPLOS. He has presided over over 20 projects, including those funded by the National Key R&D Program and key projects of the National Natural Science Foundation of China. His work on "Software-Defined Cloud Computing Resource Management Platform" won the 2021 Tianjin Science and Technology Progress Special Prize (first author), and his work on "Research on Multimedia Transmission and Protection Mechanisms" won the 2010 Ministry of Education Natural Science Second Prize.

 

Report Title: Research on the "Perception-Computation-Decision" Mechanism for Human-Computer Interaction

 

Report Abstract: 

        In the new era of deep integration of man, machine, and objects, the digital and physical worlds are interpenetrating and coevolving at an unprecedented rate. This report focuses on smart life scenarios and proposes a new theory of human-computer interaction from the three levels of "perception-computing-decision-making". It mainly includes ubiquitous multimodal wireless perception fusion theory, distributed knowledge aggregation and collaborative computing strategy, and a multi-agent framework that decouples resources and intelligence, providing more efficient and reliable solutions for smart life.

Li Xiangyang

 University of Science and Technology of China

Introduction:
        Professor and doctoral supervisor at the School of Computer Science and Technology, University of Science and Technology of China. Currently Executive Dean of the School of Information and Intelligence, Executive Dean of the School of Computer Science, and Director of the Key Laboratory of Wireless Optoelectronic Communications, Chinese Academy of Sciences. ACM/IEEE Fellow, National Outstanding Young Scientist, Chief Scientist of the National Key R&D Program, and Chair of the ACM China Steering Committee. Formerly a Professor at Illinois Institute of Technology, a Visiting Professor at Microsoft Research Asia, and a Chair Professor at Tsinghua University. He has long been engaged in research in intelligent IoT, edge computing, data security, and privacy. He has published nearly 500 international papers (including over 170 CCF A papers), 16 MobiCom papers, over 24,000 Google Scholar citations, and an H-index of 81. He has presided over over 20 national key projects, supervised over 50 doctoral students, and was selected as a member of the "Huang Danian Faculty Team."

 

Report Title: Industrial Intelligence in Discrete Manufacturing: A Look at Vertical Domain Optimization and Common Key AI Technologies

 

Report Summary:

        The discrete manufacturing industry is currently facing demands for scale and flexible production. This requires rapid response and efficient manufacturing for high-variety, small-batch orders through flexible production systems and intelligent management. The Industrial Internet is a next-generation intelligent network formed by the deep integration of industrial production systems and internet intelligence. Its core lies in the deep fusion and application of information and physical systems, encompassing perception, cognition, analysis, decision-making, and control. As core enablers of the Industrial Internet, next-generation technologies such as the Intelligent Internet of Things, edge-edge cloud computing, and artificial intelligence are profoundly transforming every aspect of industrial production. In this report, I will share how to build AI models that incorporate industry knowledge, how to build efficient AI, and how to optimize decision-making through technologies such as intelligent solvers. I will also share some of our team's initial achievements and explorations in the theory, technology, systems, and applications of industrial intelligence in discrete manufacturing. These include the generation and efficient governance and management of high-quality, multi-source, heterogeneous, multi-modal, and cross-domain isolated data in manufacturing environments; the design of large-scale intelligent manufacturing industry models driven by mechanistic knowledge and data; and the design of collaborative reasoning mechanisms for large and small models to optimize decision-making efficiency and effectiveness.

Xiao Bin

 Chongqing University of Posts and Telecommunications

Introduction:
      Professor and doctoral supervisor at Chongqing University of Posts and Telecommunications. He has been selected as a Distinguished Professor of the Ministry of Education's Changjiang Scholars Program, a Young Scholar of the Ministry of Education's Changjiang Scholars Program, a Chongqing Science and Technology Leader, a Chongqing Outstanding Young Scientist, a Distinguished Professor of the Bayu Scholars Program, and a Young Elite Talent of the Chongqing Talent Program. He serves as Deputy Director of the Ministry of Education's Key Laboratory of Cyberspace Big Data Intelligent Security and Deputy Director of the Chongqing Key Laboratory of Image Cognition. His research and teaching interests primarily focus on computer vision, medical image analysis, deep learning theory and applications, and data security and privacy protection. He has been granted 11 national invention patents and three US invention patents. His research has been recognized with awards such as the Second Prize of Chongqing Natural Science, the First Prize of the Wu Wenjun Artificial Intelligence Science and Technology Progress Award, the First Prize of Chongqing Science and Technology Progress Award, and the First Prize of the Natural Science Award of the China Computer Society.

 

Report Title: Generative Image Detection Theory and Methods

 

Report Introduction:

        With the disruptive advancement of generative artificial intelligence technology, malicious incidents caused by generated images have become a major challenge in global digital security. Current mainstream generative image detection algorithms generally suffer from bottlenecks such as limited detection accuracy, insufficient cross-domain generalization, lack of multimodal processing mechanisms, and weak interpretability, making them difficult to meet the practical needs of large-scale deployment in real-world scenarios. This report focuses on the team's thinking and work on improving image detection accuracy, multimodal collaborative detection, enhancing detection robustness, and model traceability.