Jessica Dae
Papers
1
Total Citations
4
H-Index
1
About
Jessica Dae is a rising researcher in the field of surgical robotics and human-computer interaction, with a focused interest in the perceptual challenges of robotic-assisted surgery. Her most-cited work, "Assessing visualization in robotic-assisted surgery: demystifying a misty lens" (2022), has garnered 4 citations, establishing her as a thoughtful investigator of the visual and cognitive barriers that surgeons face during minimally invasive procedures. In this paper, Dae systematically evaluates how fogging and lens clarity impact surgical performance, proposing novel metrics and solutions to enhance real-time visualization. Her contributions bridge engineering and clinical practice, aiming to improve patient outcomes by refining the surgeon’s visual feedback loop. Though early in her career, Dae’s work signals a commitment to demystifying complex operational challenges in high-stakes environments. Her research has been recognized for its practical relevance, offering a foundation for future innovations in surgical imaging and ergonomic design. For students and researchers, Dae’s trajectory exemplifies how targeted, problem-driven inquiry can yield meaningful insights in cutting-edge medical technology.
Research Focus
Key Achievements
Top Papers
- 1