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About

Yingjie Yan is a versatile researcher whose work spans two distinct but equally innovative domains: intelligent robotic inspection systems for power infrastructure and robot-assisted surgical navigation. In the field of substation automation, Yan has made significant contributions to advancing inspection robotics beyond basic perceptual functions toward genuine cognitive intelligence. His landmark 2022 paper on active pose relocalization (25 citations) addressed the critical challenge of achieving consistent image capture during routine electrical equipment inspections, while his work on human-level concept learning for object recognition (13 citations) pushed inspection robots toward defect detection capabilities that rival human cognition. More recently, his BIM-based 3D multimodal reconstruction research is transforming how vast quantities of inspection imagery are processed and utilized. Simultaneously, Yan has pioneered robot-assisted surgical navigation for craniofacial procedures, particularly distraction osteogenesis in patients with hemifacial microsomia. Through rigorous animal model experiments, feasibility studies, and early-phase clinical trials (each garnering 9 citations), he has demonstrated both the accuracy and safety of electromagnetic navigation systems in mandibular surgery. Yan's interdisciplinary breadth and commitment to translating robotic intelligence into real-world applications make him a compelling figure in applied robotics research.