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About

Rana Azzam is a robotics and computer vision researcher whose work spans autonomous perception, neuromorphic sensing, and deep learning for robotic systems. Her research addresses some of the most demanding challenges in modern robotics: enabling machines to reliably perceive and navigate complex, real-world environments. Azzam's most-cited contribution, CM-UNet (2022, 12 citations), advances unknown object segmentation in cluttered scenes — a critical capability for vision-based robotic grasping. Complementing this, her work on pose-graph neural networks for 2D SLAM (2021, 10 citations) tackles global optimality prediction in simultaneous localization and mapping, with direct implications for safety-critical autonomous systems. A distinctive thread in her research is neuromorphic vision — leveraging event cameras and brain-inspired sensing architectures. Her NeuTac (2024, 6 citations) and graph transformer-based motion segmentation work (2024, 5 citations) push the boundaries of tactile and dynamic scene perception under challenging conditions. Her UAV navigation research further demonstrates her command of sim-to-real transfer learning for aerial robotics. Across her still-emerging career, Azzam has built a cohesive body of work bridging cutting-edge sensing technologies and intelligent perception algorithms — making her a noteworthy voice in next-generation robotic autonomy research.