Jessica Ji

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About

Jessica Ji is a leading researcher in the field of robot-assisted surgery, with a primary focus on automating critical sub-tasks to enhance surgical precision and reduce clinician burden. Her work uniquely bridges computer vision and robotic kinematics, targeting two major challenges: autonomous camera control and optimized suture planning. In her highly cited 2018 work, "Learning 2D Surgical Camera Motion From Demonstrations" (30 citations), Ji pioneered a learning-based approach for automatic viewpoint selection, enabling surgical robots to intelligently frame anatomical features without manual intervention. Building on this, her 2019 paper "Optimizing Robot-Assisted Surgery Suture Plans to Avoid Joint Limits and Singularities" (15 citations) introduced the Circle Suture Placement Problem, a novel optimization framework that strategically positions stay sutures on surgical mesh to prevent robotic arm joint limits and singularities—a key source of intraoperative errors and prolonged operating times. By formulating and solving this geometric constraint problem, Ji has laid the groundwork for more reliable, autonomous suturing in laparoscopic systems like the da Vinci Research Kit. Her contributions are vital for the next generation of semi-autonomous surgical robots, promising safer and more efficient procedures.