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About

Zaynab El Mawas is a researcher specializing in multi-robot systems, cooperative localization, and fault-tolerant navigation. Her work addresses the critical challenge of ensuring safety and reliability in decentralized multi-vehicle networks, where sensor faults can compromise positioning integrity. She has pioneered hybrid model/data-driven approaches for fault detection and exclusion, integrating information theory—such as Jensen-Shannon divergence—with machine learning to diagnose and mitigate sensor failures in real time. Her most-cited paper (2022, 8 citations) introduces a fault-tolerant cooperative localization method using diagnosis based on Jensen-Shannon divergence, while subsequent works (2023, 3–5 citations each) explore decision tree-based diagnosis, federated learning for sensor diagnosis, and comparative analyses of centralized versus federated learning techniques. Her 2025 paper extends these ideas with diagnostic decision-making combining information theory and learning models. El Mawas’s contributions are vital for advancing safe autonomous navigation in applications like search-and-rescue, drone swarms, and autonomous vehicles. Her research bridges theoretical diagnostics and practical deployment, earning recognition for enhancing the robustness of multi-robot localization systems.