Hai Jin 

Professor, School of Computer Science and Technology
Director, National Engineering Research Center for Big Data Technology and System
Director, Services Computing Technology and System Lab
Director, Cluster and Grid Computing Lab
Huazhong University of Science and Technology

Hai Jin is a Chair Professor of computer science and engineering at Huazhong University of Science and Technology (HUST) in China. Jin received his PhD in computer engineering from HUST in 1994. In 1996, he was awarded a German Academic Exchange Service fellowship to visit the Technical University of Chemnitz in Germany. Jin worked at The University of Hong Kong between 1998 and 2000, and as a visiting scholar at the University of Southern California between 1999 and 2000. He was awarded Excellent Youth Award from the National Science Foundation of China in 2001. 
Jin is a Fellow of ACM, Fellow of IEEE, Fellow of CCF. He has co-authored more than 20 books and published over 1000 research papers. His research interests include computer architecture, parallel and distributed computing, big data processing, data storage, and system security.

Title: A Unified Approach of Graph Computing: From Foundations to Systems and Beyond

Abstract: Graph computing has become a fundamental paradigm for modeling complex relational data, supporting applications from large-scale analytics to modern AI. However, it remains fragmented across diverse paradigms, such as traversal, mining, learning, and querying, leading to challenges in programmability, efficiency, and scalability under extreme sparsity. This talk presents a unified approach based on an abstraction-first, hardware–software co-design methodology. We introduce a sparse matrix–based abstraction that enables unified representation and optimization. Building on this foundation, we propose a dataflow-driven architecture that addresses key bottlenecks through conflict-free pipelines, sparsity-aware caching, and scalable interconnects, achieving order-of-magnitude performance improvements. Finally, we outline a vision of graph computing as a unifying substrate for emerging workloads, particularly large model inference, enabling the convergence of data analytics, AI, and scientific computing into a single high-performance framework.