Sharan Ravigopal

With a strong foundation in both robotics and computer vision (Computer Vision for image-guided surgical robotics), I am driven to develop innovative solutions in many fields.

I am a recent PhD graduate in image-guided surgical robotics field, with extensive experience in computer vision and fabrication and control of micro-scale robotics.

In my role as a Robotics and Computer Vision Researcher at the Medical Robotics and Automation (RoboMed) Lab in the Department of Biomedical Engineering, I led groundbreaking research in the design and control of micro-scale medical robots. I developed cutting-edge computer vision models for semantic segmentation of medical devices, resulting in over 80% accuracy in localization. My work significantly decreased deep learning model run-time, enhancing efficiency without sacrificing accuracy.

-Worked on the design, modeling, and control of the world's smallest tendon-actuated robotically steerable guidewire (0.27mm)

• Developed computer vision models for semantic segmentation of devices under medical imaging like fluoroscopy and ultrasound using convolutional neural networks trained on thousands of images towards an accuracy of over 80% for localization of guidewires

-Decreased run-time of deep learning models through model pruning and transfer learning from pre-trained networks for computer vision to reduce run-time from 2.4 seconds to less than 0.07 seconds without sacrificing accuracy

• Developed 3D reconstruction algorithms for C-arm fluoroscopy encoder-decoder networks for de-noising

-8 first-author Nature and IEEE journal and conference publications, and more co-authored publications

-Femtosecond laser micromachining for fabrication of micro-scale medical robots

-Instrumental in purchases of lab equipment of over $100k

-Continuum robot modeling for tendon-driven and concentric tube robots to comply with experiments

-Extensive knowledge of mathematical models and data analysis from a compilation of experiments

-Contributed to the development of a forward-viewing ultrasound transducer integrated guidewire and synthetic aperture beamforming for improving SNR by 30%

-Conducted 300+ lab experiments for model validation and surgical guidewire traversal

Georgia Institute of Technology

PhD Robotics

MS Electrical and Computer Engineering

MS Mechanical Engineering

BS Mechanical Engineering, minor in Computer Science