Google Scholar
Education
Brown Unversity B.Sc. 2015
ENS Paris-Saclay MS, Applied Math 2016
Johns Hopkins University MSE 2020
Johns Hopkins University PhD 2021
About
Jie Ying’s work explores using machine learning and augmented reality (AR) techniques to improve surgical precision through image guidance. The techniques her group works on include using computer vision and tissue deformation models to reconstruct patient anatomy, creating photorealistic simulations, qualitative analysis for both human-centered AR design and ethical surgical automation, surgical gesture recognition, and sensorless force estimation. Application areas span improving tumor resection in head and neck surgery, ensuring complete kidney stone removal, automation of central airway obstruction and prostate surgeries, and building mixed reality low-cost surgical trainers. Lastly, her group contributes actively to the open-source surgical robotics research platform, the da Vinci Research Kit.
Research Focus
3D reconstruction, augmented reality, machine learning, computer vision, deformation modeling, sensor fusion, surgical skill assessment, surgical gesture modeling, robotics, automation
Publication Highlight
-Perseus: perception with semantic endoscopic understanding and SLAM
-All-in-One Augmented Reality Guided Head and Neck Tumor Resection
-From Sight to Skill: A Surgeon-Centered Augmented Reality System for Ureteroscopy Training