About
I am a researcher in machine learning and mathematics.
Until recently, I was a postdoctoral research associate in the Cambridge
Image Analysis group at the Department of Applied Mathematics and Theoretical Physics at the University of
Cambridge, where I completed my PhD (Structure-preserving
machine learning for inverse problems
) in
2021 under the supervision of prof. Carola-Bibiane
Schönlieb and dr Matthias J. Ehrhardt.
Before that, I took Part III of the Mathematical Tripos in Cambridge, graduating with a distinction, and I studied mathematics and physics at the undergraduate level at the Radboud University in Nijmegen, graduating summa cum laude in both subjects.
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. I am currently working on extending these ideas to scientific machine learning more generally, with an interest in neural PDE and ODE solvers. For more details about my research interests, check out this page.
An up to date list of my publications can be found at my Google Scholar page, while my contributions to open-source software can be found on my GitHub page.
I can be reached at the email address below: