We focus on resource scheduling in edge-cloud environments. Our research explores learning-based and context-aware strategies to optimize resource allocation, service deployment and task offloading. We aim to enhance system efficiency and service quality in dynamic, large-scale distributed devices.
Read MoreWe focus on the problem of intelligent collaborative model inference and dynamic scheduling across cloud, edge, and device nodes, aiming to significantly enhance the efficiency of intelligent task processing and real-time system responsiveness among distributed computing resources.
Read MoreWe focus on predictive modeling to enable proactive resource management in cloud and network systems. It leverages advanced learning-based algorithms, multi-feature fusion techniques, and cloud–edge collaborative strategies to anticipate future workloads and network conditions.
Read MoreWe focus on the co-optimization of algorithms and systems for federated learning in edge environments. We target large-scale model training over non-IID data, aiming to improve model performance, system efficiency, and collaboration across edge and terminal devices while preserving data privacy.
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"We are interested in enabling the reliable flows of useful bits in the air, the computing power in your hand, and anywhere and anytime ubiquitous communications." - ACM SIGMOBILE.
—— I like the world with wireless communications and mobile networks. And also I enjoy to see that current networks are evolving to get more and more intelligent. Furthermore, I believe that students are good teachers. Whenver you have any good ideas, we can discuss and explore the fantasy together!
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