Papers
8
Total Citations
269
H-Index
8
About
Sahba Aghajani Pedram is a pioneering researcher at the intersection of surgical robotics, autonomous systems, and medical imaging, whose work is reshaping the future of robot-assisted surgery. Her most influential contributions center on autonomous suturing, where she has developed sophisticated optimization frameworks for selecting needle parameters and planning suture paths — work that has garnered over 78 and 65 citations respectively, establishing her as a leading voice in surgical automation. Her research extends to continuum manipulator modeling, notably through her SCADE algorithm (46 citations), which simultaneously addresses sensor calibration and deformation estimation in flexible surgical instruments. Aghajani Pedram has further advanced surgical intelligence through deep learning-based temporal segmentation of surgical sub-tasks, enabling robots to perceive and respond to complex operative sequences in real time. Her more recent investigations explore OCT-guided robotic lens extraction and reinforcement learning for deformable tissue manipulation, demonstrating a remarkable breadth of vision. Across her portfolio, she consistently bridges fundamental robotics theory with clinically meaningful applications, offering solutions that could substantially reduce surgical trauma, improve precision, and democratize access to minimally invasive procedures worldwide.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8