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Department of Applied Mathematics and Theoretical Physics



  • 2021-now: Postdoctoral research associate in the Cambridge Image Analysis group,
  • 2016-2021: PhD in applied mathematics, in the Cambridge Image Analysis group,
  • 2015-2016:¬†Part III, focused on statistics and applied analysis,
  • 2012-2015: Undergraduate degrees in mathematics and physics.


My main research interest is in studying how to build principled (i.e. incorporating desired symmetries, robustness etc.) machine learning and deep learning approaches to solving ill-posed inverse problems, such as those which arise in medical image reconstruction (e.g. MRI and CT). Much of my work in this direction draws inspiration from structure-preserving numerical methods. Besides this I have a broad interest in applied mathematics in general.


Structure-preserving machine learning for inverse problems
F Sherry
Structure-preserving deep learning
E Celledoni, MJ Ehrhardt, C Etmann, RI Mclachlan, B Owren, CB Schonlieb, F Sherry
– European Journal of Applied Mathematics
Learning the Sampling Pattern for MRI
F Sherry, M Benning, JC De Los Reyes, MJ Graves, G Maierhofer, G Williams, C-B Schonlieb, MJ Ehrhardt
– IEEE transactions on medical imaging
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Research Group

Cambridge Image Analysis

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