Kejiang Ye

Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology

Papers

Total Citations

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About

Kejiang Ye is a leading researcher in cloud computing and robotic mixed reality, whose work has significantly advanced the efficiency of distributed systems. His primary research areas include microservice scaling, reinforcement learning for cloud optimization, and cross-layer communication for mixed reality environments. Ye’s most impactful contribution is "CoScal: Multifaceted Scaling of Microservices With Reinforcement Learning" (2022, 62 citations), which addresses the critical challenge of performance in microservice architectures by introducing a novel reinforcement learning approach for multifaceted scaling. This work has become a foundational reference for researchers tackling the complexity of lightweight, fine-grained cloud services. More recently, Ye has pushed the boundaries of robotic mixed reality with "Communication Efficient Robotic Mixed Reality With Gaussian Splatting Cross-Layer Optimization" (2025), proposing GSMR—a technique that reduces wireless communication costs by enabling simulators to render images opportunistically. This innovative cross-layer optimization promises to make low-cost, high-fidelity mixed reality feasible for robotics. Through these contributions, Ye has demonstrated a consistent ability to identify and solve pressing performance bottlenecks in modern computing systems, earning recognition as a key innovator in both cloud infrastructure and emerging mixed reality technologies.