MohismLab

University of Macau Mohism Laboratory
Focus on cutting-edge research in machine learning, surgical robotics, multi-robot systems, and medical artificial intelligence.

Address: E11 - 4065 Faculty of Science and Technology, University of Macau
Contact Email: qingbiaoli@um.edu.mo

Laboratory Overview

Lab Information

Name: MohismLab

Address: E11 - 4065 Faculty of Science and Technology
University of Macau

Research Areas: Machine learning-based motion control, open-source technology, intelligent medical imaging, surgical robotics, medical systems, and multi-robot systems

Research Directions

• Machine Learning-based Motion Control

• Healthcare Imaging / Intelligent Agent

• Surgical Robotics and Medical Systems

• Multi-Robot Systems

• Open-source Technology

Laboratory Director

Prof. Qingbiao Li

Qingbiao Li

Chief Scientist at Shangxian Zhiqi, Ph.D. from University of Cambridge, Assistant Professor at Faculty of Science and Technology, University of Macau

Main Responsibilities

Leading the design of AI education infrastructure, building international academic collaboration networks, promoting AI-powered curriculum systems, and publishing in top academic conferences.

Education & Experience

  • Ph.D. from University of Cambridge under Prof. Amanda Prorok, focusing on perception-communication-motion co-planning for multi-robot systems
  • Former Postdoctoral Researcher at Oxford Robotics Institute, University of Oxford; Research Assistant at Imperial College London (medical robotics)
  • MSc from Imperial College London (medical robotics); BEng in Mechanical Engineering through the "2+2" joint program of University of Edinburgh and South China University of Technology

Research Achievements

  • Publications in Nature Machine Intelligence, IEEE RA-L, IJCARS, ICRA, ICLR, and other top journals and conferences
  • Winner of the 2020 Wiseman Prize (University of Cambridge Computer Lab), 2023 National Award for Outstanding Self-financed Students Abroad
  • Over 1,000 citations on Google Scholar

Research Areas

Surgical Robotics

Focuses on intraoperative imaging machine learning technologies for surgical robots, developing intelligent surgical assistance systems to improve precision and safety.

Multi-Robot Systems

Studies perception-communication-motion co-planning for multi-robot systems, enabling intelligent collaboration among robot groups.

Intelligent Medical Imaging

Applies machine learning to medical diagnosis, developing tools such as blood oxygen saturation estimation algorithms for medical assistance.

Machine Learning-based Motion Control

Researches motion control algorithms based on machine learning to enhance autonomy and adaptability of robotic systems.