Mehrdad Zadeh
Papers
8
Total Citations
119
H-Index
6
About
Dr. Mehrdad Zadeh is a leading researcher at the intersection of haptics, robotics, and surgical training, whose work is fundamentally reshaping how humans and machines collaborate in high-stakes medical environments. His primary research areas include haptic feedback systems, physical human-robot interaction (pHRI), and skill transfer in robot-assisted surgery. Dr. Zadeh’s most significant contribution is the development of intelligent, adaptive haptic guidance frameworks that learn from user performance to optimize force feedback in real time. His seminal 2013 paper on a direct optical force-sensing solution for haptic rendering in minimally invasive surgery (37 citations) established a foundational architecture for providing surgeons with realistic force feedback during teleoperation. He further advanced the field with innovative work on gesture-based adaptive guidance using discriminative modeling (12 citations) and model-predictive control approaches for shared control (6 citations). Notably, his 2019 study on learning-based guidance for orthopaedic surgical drilling skill (20 citations) demonstrates a novel pathway for transferring expert knowledge to trainees through physical interaction. With over 100 total citations across his portfolio, Dr. Zadeh’s research is pivotal in creating personalized, intelligent robotic training systems that promise to accelerate surgical skill acquisition and improve patient outcomes.
Research Focus
Key Achievements
Top Papers
- 1
- 2Advances in Haptics25 citations · 2010
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- 4
- 5Model-Based Haptic Guidance in Surgical Skill Improvement9 citations · 2015
- 6
- 7
- 8Localization of annulus with a tactile sensor2 citations · 2011