Mohamad Qadri

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About

Mohamad Qadri is a roboticist whose research lies at the intersection of hardware-software co-design, perception, and state estimation for autonomous systems. His most impactful work, "RACOD" (19 citations), introduces a novel algorithm and hardware accelerator for mobile robot path planning, featuring CODAcc—a MapReduce-style collision detection accelerator—and RASExp, a runahead path exploration extension. In underwater robotics, Qadri pioneered the use of Conditional GANs for sonar image filtering (10 citations), enabling robust occupancy mapping from noisy acoustic data—a critical advance for subsea autonomy. He has also developed a bilevel optimization framework for learning error covariances in state estimation (4 citations), directly addressing the tuning challenges that limit estimator convergence. His work on semantic feature matching for agricultural SLAM (4 citations) tackles texture-poor environments, enhancing mapping reliability for field robots. Across these contributions, Qadri demonstrates a rare ability to span from low-level hardware acceleration to high-level perceptual reasoning, consistently pushing the boundaries of what autonomous robots can achieve in unstructured, noisy, and dynamic environments.