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About

Jie Tang is a robotics researcher whose work spans computer vision, medical robotics, and robotic manipulation, with particular emphasis on advancing autonomous surgical systems and flexible robotic platforms. Tang's early contributions focused on object recognition and perception, most notably developing a textured object recognition pipeline combining color and depth data, which has garnered 117 citations and remains influential in robotic perception. His work on modeling deformable one-dimensional objects (48 citations) laid important groundwork for manipulation tasks relevant to surgical suturing and medical applications. Tang's research trajectory has increasingly centered on medical robotics innovation. He has made significant contributions to continuum robot design, developing extensible and contractible tendon-driven robots for neurosurgery that navigate confined anatomical spaces with exceptional dexterity. His autonomous surgical assistance systems — including robots for blood removal during craniotomy and saliva aspiration in oral surgery — demonstrate a commitment to reducing clinician workload and improving patient safety. His steerable biopsy needle system with real-time resistivity monitoring (16 citations) reflects sophisticated sensor integration for precision diagnosis. Collectively, Tang's portfolio reveals a researcher who bridges perception, mechanical design, and clinical application, steadily building toward fully autonomous surgical robotics that could meaningfully transform operating room practice.