Associate Professor at Sun Yat-sen University , China
Dr. Weibing Li is an Associate Professor at the School of Computer Science and Engineering, Sun Yat-sen University, China. He holds a Ph.D. in Mechanical Engineering from the University of Leeds, UK (2018) and specializes in robotics 🤖, neural networks 🧠, and optimization 🔢. His research focuses on industrial and medical robotics, deep learning, and nonlinear programming. A recipient of multiple national and regional research grants, he has published extensively in top-tier IEEE journals. Dr. Li also worked as a Postdoctoral Researcher at The Chinese University of Hong Kong. 📧
Publication Profile
Orcid
🎓 Academic Background
Dr. Weibing Li holds a Ph.D. in Mechanical Engineering (2018) from the University of Leeds, UK 🇬🇧, where he worked under Prof. Robert Richardson and Prof. Jongrae Kim. He earned his M.S. in Detection Technology and Automatic Equipment (2014) from Sun Yat-sen University, China 🇨🇳, guided by Prof. Yunong Zhang, and a B.S. in Communication Engineering (2011) from Changchun University. His research focuses on robotics 🤖 (industrial, medical, modular), neural networks 🧠 (deep learning, reinforcement learning), and operations research 🔢 (optimization, nonlinear programming, cybernetics), contributing significantly to AI-driven robotic applications. 🚀
👨🏫 Professional Experience
Dr. Weibing Li is an Associate Professor at the School of Computer Science and Engineering, Sun Yat-sen University 🇨🇳 (since 2020), where he advances research in robotics and AI. Previously, he was a Postdoctoral Researcher at the Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong 🇭🇰 (2018-2020), working under Prof. Zheng Li on medical robotics. He also served as a Robotics Assistant at the University of Leeds, UK 🇬🇧 (2017-2018), contributing to advanced robotic systems under Prof. Robert C. Richardson. His expertise spans intelligent automation, neural computation, and robotic control. 🤖🚀
🔬 Research Focus Areas
Dr. Weibing Li’s research spans robotics 🤖, neural networks 🧠, and operations research 📊. His work in industrial, medical, and modular robotics focuses on intelligent control and automation. In neural networks, he explores deep learning, reinforcement learning, and neural computation. His contributions to operations research and cybernetics include optimization techniques like linear and nonlinear programming. He leads multiple projects, including research on robotic endoscopes, intelligent control algorithms, and recurrent neural networks. His work enhances robotic precision, human-robot interaction, and AI-driven automation, contributing significantly to robotic intelligence and healthcare technologies. 🚀
Publication Top Notes
1️⃣ An Accelerated Anti-Noise Adaptive Neural Network for Robotic Flexible Endoscope With Multitype Surgical Objectives and Constraints 🏥🤖
Year: 2025 | Publisher: IEEE
2️⃣ A Quadratic Programming Solution to Position-Level Repetitive Motion Planning and Obstacle Avoidance of Joint-Constrained Robots 📏🤖
Year: 2025 | Publisher: IEEE
3️⃣ A Cerebellar Model Articulation Controller Enhanced Neural Solution to Time-Varying Linear Equations With Robotic Application 🧠🤖
Year: 2024 | Publisher: IEEE
4️⃣ X-Ray-Based Three-Dimensional Shape Reconstruction Method for a Continuum Manipulator 📸🤖
Year: 2024 | Publisher: IEEE
5️⃣ Visual Exploration-Enhanced Quadruped Robot with Active-Passive Composite Telescope Mechanism 🦿🤖
Year: 2024 | Publisher: IEEE