Research
My primary research focus is in inverse problems, focusing on data-driven regularisation methods. I am intrigued by inverse problems (specifically those found in imaging applications such as in CT or MRI scans), Machine Learning and numerical optimisation. My PhD seeks to learn data driven techniques to help solve inverse problems derived from a variational regularisation modelling approach.
The PhD is under the supervision of Dr. Yury Korolev and Dr. Matthias Ehrhardt. More generally, my interests span numerical analysis, mathematical modelling and machine learning (ML).
Publications
Degree projects
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Plug-and-play regularisation techniques for imaging inverse problems (
PDF).
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Deep learning for Stokes-Brinkman equations (multi-scale porous media flow) (
PDF).
Work completed as part of an Interdisciplinary Research Project during my MRes at the University of Bath. Supervised by Dr.
James Foster, Dr.
Yang Chen.
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Asymptotic and hyperasymptotic expansions of solutions to ordinary differential equations (ODEs) (
PDF).
Written as a masters project for my MSci degree at UCL, supervised by Prof.
Rod Halburd.
Industry Research Projects
During my PhD at University of Bath, I worked in interdisciplinary teams to formulate mathematical problems from high-level applied challenges in collaboration with industrial partners. These events are known as Integrative Think Tanks (
ITTs):
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ITT21, January 2025.
Industrial Partners: Diamond Light Source/Ada Lovelace Centre and Novartis .
Project title: "Learned regularisation with plug-and-play for ptychography" (slides)
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ITT20, June 2024.
Industrial Partners: BeZero Carbon and Rolls-Royce Holdings.
Project title: "Reverse engineering atmospheric dust content from jet engine samples" (slides)
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ITT19, February 2024.
Industrial Partners: Wessex Water and CameraForensics .
Project title: "Physics-based models using partial differential equations for modelling river velocity and bacteria concentrations" (slides)