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About

Mariam Faied is a robotics and autonomous systems researcher whose work sits at the intersection of search and rescue robotics, wireless communications, and intelligent navigation. Her research has made meaningful contributions to the deployment of autonomous robots in disaster environments, addressing the critical challenge of keeping human responders out of harm's way during emergencies such as earthquakes and hurricanes. Faied's most influential work focuses on LoRa LPWAN-based communication architectures for search and rescue (SAR) missions, earning 24 citations, alongside complementary disaster management systems and robot platooning strategies that collectively demonstrate her sustained commitment to practical, life-saving robotics solutions. Her fuzzy-based accessibility model for disaster navigation (20 citations) showcases her skill in applying intelligent decision-making frameworks to real-world autonomous robot traversal challenges. Beyond disaster robotics, Faied has contributed to deep learning-based traffic sign recognition (14 citations), kinodynamic trajectory planning using Bézier contours, and distributed heterogeneous control of aerial and ground robots — reflecting a broad command of autonomous systems. Her earlier work on self-driving robot simulation and mobile manipulation further underscores her versatility. With over 100 cumulative citations, Faied is an emerging voice in field robotics, particularly where autonomous systems must operate reliably in unpredictable, high-stakes environments.