Career
- 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.
Research
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.
Publications
Convergent Regularization in Inverse Problems and Linear Plug-and-Play Denoisers
– Foundations of Computational Mathematics
(2024)
1
(doi: 10.1007/s10208-024-09654-x)
Designing stable neural networks using convex analysis and ODEs
– Physica D Nonlinear Phenomena
(2024)
463,
134159
(doi: 10.1016/j.physd.2024.134159)
Dynamical Systems–Based Neural Networks
– SIAM Journal on Scientific Computing
(2023)
45,
A3071
(doi: 10.1137/22m1527337)
Convergent regularization in inverse problems and linear plug-and-play
denoisers
(2023)
Imaging With Equivariant Deep Learning: From unrolled network design to fully unsupervised learning
– IEEE Signal Processing Magazine
(2023)
40,
134
(doi: 10.1109/MSP.2022.3205430)
Equivariant neural networks for inverse problems
– Inverse problems
(2021)
37,
085006
(doi: 10.1088/1361-6420/ac104f)
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