Papers
2
Total Citations
181
H-Index
2
About
Ruixuan Li is a leading researcher at the intersection of artificial intelligence, edge computing, and robotic-assisted medical systems. Their work primarily focuses on optimizing resource allocation in complex, real-time environments—a challenge central to both Industry 4.0 and next-generation surgical robotics. Li’s most impactful contribution, the highly cited 2020 paper on “Multiagent Deep Reinforcement Learning for Joint Multichannel Access and Task Offloading of Mobile-Edge Computing in Industry 4.0” (176 citations), pioneers a novel multiagent deep reinforcement learning framework. This work enables intelligent, decentralized decision-making for task offloading and channel access in mobile-edge computing (MEC) networks, a critical enabler for low-latency, autonomous industrial systems. Beyond this, Li has advanced surgical technology with their 2022 study on “Robot-assisted Ultrasound Reconstruction for Spine Surgery,” which translates bench-top algorithms into pre-clinical validation—demonstrating the robustness of robotic ultrasound systems for non-radiative 3D spinal guidance. By bridging theoretical AI models with practical, life-saving applications, Li’s research not only drives foundational progress in multiagent systems and edge intelligence but also directly impacts patient care and industrial automation, marking them as a versatile and impactful contributor to both computer science and biomedical engineering.
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