Kefan Song

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About

Kefan Song is a robotics researcher whose work centers on the intersection of high-precision mechanisms and medical instrumentation, with a particular focus on ophthalmic and interventional applications. His primary research areas include kinematic calibration for surgical robots and the development of automated calibration systems for sensorized medical tools. Song’s most notable contribution is his work on the Steady-Hand Eye Robot (SHER), where he derived a linear error model for delta robot kinematic calibration to achieve the sub-millimeter precision required for robot-assisted retinal surgery—a paper that has garnered 14 citations since 2022. He further advanced the field of prostate biopsy by developing a semi-automatic robotic calibration system for fiber Bragg grating (FBG)-sensorized flexible needles, addressing the critical bottleneck of time-consuming and error-prone manual calibration, with this 2021 work accumulating 7 citations. Song’s research demonstrates a clear commitment to translating robotic precision into tangible clinical improvements, reducing human error while enhancing procedural accuracy. His achievements position him as an emerging leader in medical robotics, with a focus on making complex surgical tasks safer and more reliable through intelligent automation.