Navid Masoumi
Papers
2
Total Citations
4
H-Index
2
About
Navid Masoumi is a rising researcher at the intersection of soft robotics and intelligent sensing, with a core focus on developing embedded force and shape sensors for minimally invasive surgical applications. His major contributions center on overcoming the inherent non-linearity and noise challenges of soft sensors through deep learning-based calibration methods. In his 2024 work, Masoumi introduced a novel gelatin-graphite force sensor with high compliance, paired with a convolutional deep learning calibration that dramatically reduces noise amplification—a critical advance for delicate intraluminal procedures. His 2023 paper, "WaveLeNet," further pioneered transfer neural calibration for soft robot sensing, enabling adaptable, high-fidelity feedback in environments like bronchoscopy and cardiovascular intervention. Though early in his career, with each of his most-cited papers garnering 2 citations, Masoumi’s work is already shaping the future of safe, intelligent surgical tools. By merging material innovation with machine learning, he is laying the groundwork for soft robots that can feel and adapt inside the human body—a transformative step toward next-generation, autonomous medical interventions.
Research Focus
Key Achievements
Top Papers
- 1
- 2