Tatiana Renna
Papers
1
Total Citations
6
H-Index
1
About
Dr. Tatiana Renna is pioneering the intersection of deep learning and fiber-optic sensing for next-generation medical robotics. Her primary research focuses on continuum robots—snake-like, elastic manipulators ideal for minimally invasive surgery—and the development of advanced shape-sensing technologies to control them with unprecedented precision. In her landmark 2021 feasibility study, Dr. Renna introduced a supervised deep-learning approach to model edge-FBG (Fiber Bragg Grating) shape sensors, a breakthrough that addresses critical challenges in surgical robotics: creating sensors that are miniature, sterile, immune to electromagnetic interference, and easily integrable. This work, which has garnered 6 citations, demonstrates her ability to harness artificial intelligence to solve complex, real-world engineering problems. By enabling accurate 3D shape reconstruction of flexible manipulators, her contributions are paving the way for safer, more dexterous surgical tools. Dr. Renna’s research stands at the forefront of soft robotics and smart sensing, offering transformative potential for clinical applications where precision and reliability are paramount.
Research Focus
Key Achievements
Top Papers
- 1