Papers
31
Total Citations
501
H-Index
11
About
Jie Ying Wu is a prominent researcher at the intersection of surgical robotics, machine learning, and computer-assisted intervention. Her work spans augmented reality in robotic-assisted surgery, force estimation, surgical gesture recognition, and simulation environments for autonomous surgical systems. Wu's most influential contribution—a 2019 review of augmented reality in robotic-assisted surgery (156 citations)—established a foundational reference for researchers exploring how AR can enhance platforms like the da Vinci Surgical System. Building on this, she has made significant strides in sensorless force estimation, developing both neural network-based inverse dynamics models and deep learning approaches to infer tool-tissue interaction forces from joint encoders and motor currents—critical capabilities in systems where haptic feedback is absent. Her research also advances surgical intelligence through multimodal learning. By fusing robotic kinematics with surgical video, Wu has contributed gesture recognition frameworks and cross-modal self-supervised learning methods that bring autonomous cognitive assistance closer to clinical reality. Her open simulation environment for suturing further addresses data scarcity challenges in surgical AI training. With over 400 cumulative citations and contributions spanning perception, control, and simulation, Wu's work is shaping the future of intelligent, data-driven robotic surgery.
Research Focus
Key Achievements
Top Papers
- 1A Review of Augmented Reality in Robotic-Assisted Surgery156 citations · 2019
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- 10NIDD: an intelligent network intrusion detection model for nursing homes14 citations · 2022