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About

Bin Liang is a versatile researcher whose work spans two distinct yet technically sophisticated domains: space robotics and medical physics, particularly robotic radiotherapy. Early in his career, Liang made meaningful contributions to space robotics, addressing fundamental challenges in teleoperation and autonomous manipulation. His work on dual-arm space robot motion trajectory generation tackled the complex problem of dynamic coupling between robotic arms and spacecraft bases, while his research on modeling human intelligence for space object capturing explored innovative, model-free approaches to tracking and grasping free-flying objects in orbit. Liang's research trajectory later shifted toward robotic radiotherapy, where he has developed novel optimization techniques for circular cone-based systems like CyberKnife. His singular value decomposition linear programming (SVDLP) method offered improved solutions to the complex beam placement challenges inherent in robotic linac systems. More recently, his application of the PyTorch deep learning toolkit to treatment plan optimization represents a forward-thinking integration of machine learning into clinical radiotherapy workflows, earning 8 citations since 2022. Collectively, Liang's interdisciplinary contributions — spanning robotics, artificial intelligence, and radiation oncology — reflect a career dedicated to solving complex optimization problems in high-stakes, precision-critical environments.