FALCONE Francescoflavio

Joint EUTOPIA PhD thesis between CY Cergy Paris University and University of Warwick

Member of team :

Neurocybernetics

Lieu :

CYU Saint-Martin

Phone :

+ 33 752970244

Biography

I hold a Bachelor’s and a Master’s degree in Biomedical Engineering from the University of Bologna. Throughout my academic path, I have developed a strong interest in Data-Driven Systems, Neuroengineering, and Artificial Intelligence applied to healthcare, which now define the core of my research. In 2025, I was selected for the D20 Leader Executive Master, an intensive program focused on leadership, project management, and innovation in industrial and healthcare sectors. In autumn 2025, I began my Joint PhD at ETIS Lab (CY Cergy Paris Université) and the University of Warwick.

My research focuses on transforming episodic clinical assessments into continuous, ecologically valid digital monitoring systems. I will develop multimodal AI pipelines to extract clinically interpretable digital biomarkers from low-cost biosignals, while using robotic systems first to model neurodegenerative conditions and then as socially assistive agents to improve patient engagement and diagnostic accuracy. This includes the use of robotic systems not only as assistive tools but also as experimental platforms to model neurodegenerative conditions and their behavioural dynamics.

Research activities

Ongoing projects

PhD Title:  HERO – A Holistic Evaluation and Assistive Robotic System for neurodegenerative diseases

Supervisors & Thesis Directors: Lola Cañamero, Davide Piaggio , Viji Ahanathapillai


Key Focus:
– Holistic Digital Assessment: Development of integrated systems combining physical, cognitive, and wellbeing data into a unified framework for continuous monitoring of neurodegenerative disorders.
– Socially Assistive Robotics (SAR): Investigation of robotic systems first as experimental platforms to model neurodegenerative conditions, and subsequently as assistive agents to enhance patient engagement and socio-emotional wellbeing.
– Interpretable AI & Biosignals: Application of machine learning to extract clinically meaningful digital biomarkers from multimodal data (e.g., speech, physiological signals), ensuring transparency and reliability.
– Human-Centred Design: Implementation of user-centred approaches to develop scalable and effective solutions for both clinical and community-based care

Publications dans Hal
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Publications