Max Robotics — Global Service Robot Directory + US Certification

About

Quazi Marufur Rahman is a researcher at the forefront of ensuring the safety and reliability of autonomous systems, with a primary focus on **robotic perception, machine learning monitoring, and computer vision for autonomous vehicles**. His major contributions lie in developing run-time monitoring frameworks that detect performance degradation in deep learning-based object detectors during real-world deployment. Notably, his work addresses the critical gap between lab-tested accuracy and on-road performance, introducing systems that can flag false negatives—such as missed traffic signs—before they lead to catastrophic failures. His most cited paper, "Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends" (2021, 62 citations), has become a foundational reference for researchers in this safety-critical domain. Rahman’s 2019 paper on a false negative alarm system for traffic sign detectors (32 citations) further underscores his impact, proposing a practical solution for one of autonomous driving’s most dangerous failure modes. Through his pioneering work, Rahman is helping to bridge the gap between cutting-edge AI and the rigorous safety demands of real-world robotics.