Elizabeth F. Wanner

Papers

Total Citations

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About

Elizabeth F. Wanner is a leading researcher at the intersection of evolutionary computation, robotics, and digital twin technologies. Her work focuses on solving complex optimisation problems in autonomous systems, particularly through multi-objective evolutionary algorithms and self-aware, scalable architectures. In her highly cited 2020 paper on phonetic-aware speech recognition, she pioneered the use of multi-objective evolutionary algorithms to optimise speech recognition systems, achieving 18 citations for this foundational contribution. That same year, she advanced mobile-cloud hybrid robotics with a self-aware, scalable solution that leverages cloud computing to meet the demands of computationally intensive robotic tasks, earning 5 citations. Most recently, in 2025, Wanner introduced DARLING—a framework for federated, autonomous, and cognitive digital twins—positioning herself at the forefront of Industry 4.0 decision-making tools. Her work bridges the gap between theoretical optimisation and practical deployment in manufacturing, healthcare, aerospace, and robotics. Wanner’s research is distinguished by its focus on autonomy, efficiency, and real-world scalability, making her a key figure in the evolution of intelligent, cloud-integrated robotic systems.