Qingbiao Li is an assistant professor and robotics researcher with nine years of experience specializing in communication-aware motion planning for multi-robot systems. Trained at Cambridge (PhD) with prior degrees from Imperial and Edinburgh, he applies Graph Neural Networks to enable learned inter-agent communication for applications like mobility-on-demand, automated warehouses, and smart cities. His work bridges theoretical control (online parameter estimation for robust bipedal walking) and practical deployment, including a Microsoft Research internship on explainable RL for Project Silica scheduling. He has held research and teaching roles at Oxford and Cambridge and led engineering projects spanning levitation systems, industrial robot optimization, and legged locomotion. Known for blending simulation and hardware experiments (ROS, AWS RoboMaker, TurtleBot), he seeks collaborative research and internship opportunities. A less obvious strength is his track record of translating control-theoretic insights into ML-driven communication protocols for coordinated robotic teams.
9 years of coding experience
2 years of employment as a software developer
Master of Engineering (M.Eng.) Mechanical Engineering, Master of Engineering (M.Eng.) Mechanical Engineering at The University of Edinburgh
Doctor of Philosophy - PhD Department of Computer Science and Technology, Doctor of Philosophy - PhD Department of Computer Science and Technology at University of Cambridge
Master of Research Medical Robotics and Image Guided Intervention , Master of Research Medical Robotics and Image Guided Intervention at Imperial College London
Bachelor's degree Mechanical Engineering, Bachelor's degree Mechanical Engineering at South China University of Technology
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