Dhruv Kool Rajamani
Papers
4
Total Citations
71
H-Index
4
About
Dhruv Kool Rajamani is pioneering the intersection of reinforcement learning and medical robotics, with a focus on automating complex surgical tasks. His work centers on developing intelligent systems for robot-assisted surgery, particularly in collaborative suturing and neurosurgical planning. Rajamani’s most cited paper, “Collaborative Suturing: A Reinforcement Learning Approach to Automate Hand-off Task in Suturing for Surgical Robots” (2020, 46 citations), introduces a novel RL framework to automate the delicate hand-off of needles during suturing—a critical step in minimally invasive procedures. He also created AMBF-RL (2022, 15 citations), a real-time simulation toolkit that provides realistic environments for training medical robots, addressing a key gap in the field. Additionally, his work on NeuroPlan (2021, 6 citations) offers a surgical planning toolkit for MRI-compatible stereotactic neurosurgery robots, enhancing precision in brain tumor ablation. Rajamani’s research extends to assessing thermal effects on brain tissue outside ablation zones (2022, 4 citations), contributing to safer therapeutic ultrasound. With over 70 total citations, his contributions are advancing autonomous surgical systems, making procedures more accurate and less invasive—a promising direction for the future of robotic surgery.
Research Focus
Key Achievements
Top Papers
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