Rania Mzid
Papers
2
Total Citations
13
H-Index
2
About
Rania Mzid is a leading researcher in the design and verification of real-time and self-adaptive systems, with a focus on model-driven engineering and intelligent control. Her work bridges the gap between high-level design models and low-level implementation on real-time operating systems (RTOS), enabling early verification of critical timing properties. In her most cited work, she introduces a reinforcement learning-based approach for the online optimal control of self-adaptive real-time systems, a novel contribution that has already garnered 10 citations since its 2023 publication. This work demonstrates her ability to integrate machine learning with real-time system design, offering dynamic, adaptive solutions for complex embedded environments. Her earlier research on model-driven methodologies for refining RTOS-independent designs into RTOS-specific models has also been influential, providing a systematic framework for ensuring timing correctness across different hardware platforms. Mzid’s contributions are essential for advancing the reliability and adaptability of modern cyber-physical systems, and her innovative use of reinforcement learning marks her as a rising figure in the field of real-time and embedded systems.
Research Focus
Key Achievements
Top Papers
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- 2