Max Robotics — Global Service Robot Directory + US Certification

About

Hooman Esfandiari is a researcher working at the intersection of medical robotics, surgical navigation, and artificial intelligence, with a focus on transforming the precision and safety of orthopedic procedures. His work addresses some of the most technically demanding challenges in spine surgery, particularly the accurate placement of pedicle screws during spinal fusion — a task requiring millimeter-level precision in close proximity to critical anatomical structures. Esfandiari's most impactful contribution, SafeRPlan, introduces safe deep reinforcement learning for intraoperative surgical planning, garnering 16 citations since its 2024 publication and representing a significant step toward trustworthy AI-guided robotic surgery. Complementing this, his domain adaptation work on 3D lumbar spine reconstruction from fluoroscopy data (10 citations) tackles real-world barriers to surgical navigation adoption, including radiation exposure and workflow integration. Earlier work on single-camera visual odometry for C-arm localization demonstrates his long-standing commitment to practical, cost-effective surgical tracking solutions. Across his career, Esfandiari has consistently bridged computer vision, robotics, and clinical application, establishing himself as an emerging voice in intelligent surgical systems with growing recognition from the research community.