Fernando E. Casado is a Research Associate at the INFORMED AI Hub, working within the Personal Robotics Laboratory (PRL), Department of Electrical and Electronic Engineering, Imperial College London. He earned his BSc in Computer Science from the University of Santiago de Compostela (USC), Spain, in 2017, receiving awards for both the best academic record and the best thesis.
In 2018, he completed the MSc in Artificial Intelligence Research from Menéndez Pelayo International University (UIMP), Spain. Fernando was awarded his PhD by USC in November 2022 with the highest grade and the Cum Laude distinction. His doctoral research focused on advancing continual federated learning strategies, addressing challenges in heterogeneous and non-stationary data scenarios, including concept drift. The proposed algorithms were translated into different applications, including human activity recognition in smartphones and active assistance to robotic wheelchair users.
In March 2023, Fernando joined the PRL as part of the UKRI Trustworthy Autonomous Systems Node on Trust project, where he developed privacy-aware and personalised learning methods for estimating user intentions and trust in human-robot interaction. Since November 2024, he has been contributing to research under INFORMED AI. His current research interests focus on distributed multi-robot and multi-user machine learning, aiming to model, adapt, and personalise robotic behaviours for assistive tasks.
Fernando’s recent work includes:
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Quesada, R. C., Casado, F. E., & Demiris, Y. (2024, August). On the Effect of Augmented-Reality Multi-User Interfaces and Shared Mental Models on Human-Robot Trust. In 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN) (pp. 1316-1322). IEEE.
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Casado, F. E., Lema, D., Iglesias, R., Regueiro, C. V., & Barro, S. (2023). Ensemble and continual federated learning for classification tasks. Machine Learning, 112(9), 3413-3453.
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Criado, M. F., Casado, F. E., Iglesias, R., Regueiro, C. V., & Barro, S. (2022). Non-IID data and continual learning processes in federated learning: A long road ahead. Information Fusion, 88, 263-280.
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Casado, F. E., & Demiris, Y. (2022, October). Federated learning from demonstration for active assistance to smart wheelchair users. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 9326-9331). IEEE.
Fernando’s work for INFORMED AI is being developed together with Prof Yiannis Demiris. You can contact Fernando at: f.estevez-casado20@imperial.ac.uk.