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Nan Fletcher-Lloyd 

PhD (She/They)

Research Assistant

Engineering Clinically Deployable AI for Dementia Risk Prediction

Biography

Nan Fletcher-Lloyd is a postdoctoral Research Associate in the UK Dementia Research Institute Care Research & Technology Centre in the Translational Machine Intelligence Lab in the Department of Brain Sciences at Imperial College London. She recently completed her PhD in machine learning for clinical applications in ageing and dementia. Before this, she completed a BSc in Biotechnology in 2020, followed by an MSc in Translational Neuroscience in 2021, both from Imperial College London. She was also selected as one of the inaugural BioFAIR fellows, a role centred on improving transparency and accessibility in UK Life Sciences to reduce barriers to participation.

Honours & awards

  • Reproducibility Prize, UK Dementia Research Institute, 2025
  • Imperial College London Student Awards for Outstanding Achievement, 2025
  • Department of Brain Sciences Award for Public Engagement, Imperial College London, 2025
  • Union Fellowship, Imperial College Union, 2024
  • Campaign of the Year - ICUsToo, Imperial College Union, 2022 

Research interest

Her research sits at the intersection of machine learning, clinical decision support, and public health, with a focus on developing interpretable and fairness-aware AI for biomedical discovery and clinically meaningful decision-making. Her work spans predictive modelling for dementia and other neurodegenerative conditions, with the aim of supporting risk assessment, earlier intervention, and responsible translation of AI in healthcare.

Key publications

NPJ digital medicine
Published
Real-world deployment of remote sleep monitoring technologies reveals distinct patterns associated with cognitive decline
Authors
Nan Fletcher-Lloyd, Nathalia Céspedes Gómez, Alexander Capstick, Antigone Fogel, Marirena Bafaloukou, Mahan Heydari, Alexandra Cairns, Chloe Walsh, Jessica True, CR T Group, Behnam Shariati, Ramin Nilforooshan, Payam Barnaghi
Real-world deployment of remote sleep monitoring technologies reveals distinct patterns associated with cognitive decline