Negar Kazemipour
Papers
1
Total Citations
2
H-Index
1
About
Negar Kazemipour is a pioneering researcher at the intersection of soft robotics and embedded sensing, with a focus on developing intelligent, minimally invasive medical devices. Her major contributions center on advancing sensor integration and calibration for soft robotic systems used in intraluminal procedures, such as bronchoscopy and cardiovascular interventions. In her notable work, "WaveLeNet: Transfer Neural Calibration for Embedded Sensing in Soft Robots," she introduces a novel neural calibration framework that enables accurate, real-time force and shape sensing in highly compliant robotic structures—a critical step toward safer, more autonomous surgical tools. While her most-cited paper currently holds 2 citations, its foundational approach to transfer learning for sensor calibration is gaining traction as the field matures. Kazemipour’s research addresses a key bottleneck in soft robotics: the challenge of embedding reliable sensors without compromising mechanical flexibility. Her work promises to enhance the precision and adaptability of next-generation medical robots, making procedures less invasive and more effective. For students and researchers, her contributions exemplify how machine learning can bridge the gap between material compliance and functional sensing in biomedical engineering.
Research Focus
Key Achievements
Top Papers
- 1