Tarik Nahhal

Verimag, University of Hassan II Casablanca

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About

Tarik Nahhal is a researcher whose work bridges formal verification, hybrid systems, and applied deep learning. His most significant contributions lie in the development of coverage-guided test generation for continuous and hybrid systems, a method that systematically explores system behaviors to uncover hidden faults. This foundational work, published in 2009 and cited 86 times, has become a key reference for engineers and scientists working on safety-critical cyber-physical systems. Nahhal also pioneered the use of randomized simulation techniques for validating analog and mixed-signal circuits, adapting the Rapidly-exploring Random Trees (RRT) algorithm from robotics to guarantee thorough coverage of circuit behaviors. This innovative approach, presented in 2006, demonstrates his ability to transfer concepts across domains—from motion planning to hardware verification. More recently, Nahhal has applied deep learning to practical challenges, developing a face-mask detection system using convolutional neural networks (2021). This work reflects his ongoing interest in leveraging AI for real-world safety applications. With a career spanning formal methods, simulation-based validation, and machine learning, Nahhal’s research continues to influence both theoretical advances and practical tools in embedded and hybrid systems.