Yash Kulkarni

Papers

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About

Yash Kulkarni is an emerging researcher at the intersection of medical robotics, tactile sensing, and biomechanical engineering, with work spanning intelligent diagnostic systems and innovative surgical hardware. His research focuses on advancing minimally invasive medical technologies through the integration of machine learning, vision-based sensing, and additive manufacturing. Among his most notable contributions, Kulkarni co-developed a Vision-based Tactile Sensor (VTS) paired with machine learning algorithms to improve endoscopic diagnosis of Advanced Gastric Cancer tumors — a pioneering approach that earned 5 citations shortly after its 2024 publication. This robot-enabled diagnostic framework addresses longstanding limitations in polyp characterization, offering a promising pathway toward more accurate, automated cancer detection. Equally significant is his work on a transformative 3D-printed flexible pedicle screw designed for robotic spinal fixation. Targeting a critical clinical challenge — screw loosening and pullout in osteoporotic patients — this research introduces biomechanically informed design solutions that could meaningfully improve outcomes for vulnerable patient populations, garnering 4 citations within its debut year. With multiple high-impact contributions in 2024 alone, Kulkarni demonstrates exceptional early-career productivity, positioning himself as a promising innovator in surgical robotics and intelligent medical device development.