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About

Raj Shekhar is a leading innovator at the intersection of surgical robotics, computer vision, and image-guided interventions. His research focuses on developing intelligent, context-aware operating rooms and advanced visualization tools for minimally invasive surgery. Shekhar’s most impactful work, the highly cited "OR 2.0" paper (103 citations), outlines a vision for context-aware surgical environments that integrate robotics, imaging, and data analytics to enhance procedural precision and safety. He has made significant contributions to deep learning for surgical instrument segmentation in laparoscopic videos, creating training datasets and frameworks that enable automated tool tracking—a critical step toward autonomous surgical assistance. In the realm of hardware innovation, Shekhar designed a magnetically anchored, wireless stereoscopic robot with optical-inertial stabilization, addressing tool clashing in single-port surgery and improving visualization. His recent work on robot-assisted ultrasound probe calibration (2025) pushes the boundaries of image-guided interventions, promising more accurate and less invasive procedures. With a portfolio spanning from foundational vision papers to cutting-edge robotic systems, Shekhar’s research is shaping the future of computer-assisted surgery, earning him recognition as a key figure in the field.