Debayan Roy

Jadavpur University, Huawei German Research Center

Papers

Total Citations

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About

Debayan Roy’s research lies at the intersection of agricultural robotics and real-time autonomous systems, with a focus on computer vision for harvesting and timing analysis for safety-critical applications. His early work pioneered statistical video tracking methods for pomegranate fruit detection in dense foliage, addressing key challenges in robotic harvesting—such as occluded fruit identification—which remains foundational for precision agriculture (7 citations). More recently, Roy has advanced the field of model-based timing analysis for ROS2-based autonomous systems, developing trace-enabled timing model synthesis that enables rigorous schedule optimization and timing verification in time-critical automotive and robotics platforms (5 citations). This work is particularly impactful as autonomous systems increasingly rely on ROS2 for production-grade deployments. Roy’s contributions bridge practical agricultural automation with the formal methods needed to guarantee real-time performance in autonomous vehicles and robots. His research demonstrates a rare ability to tackle both the perceptual challenges of unstructured environments and the temporal constraints of cyber-physical systems, making his work relevant to researchers in field robotics, real-time systems, and autonomous driving.