When not doing maths, Amin loves to do jigsaw puzzles, stay active with tennis and running long distances – some of which included the London and Brighton marathons alongside the weekly parkruns!

Research project title:
Data-driven regularisation methods with infimal convolution for inverse problems

Supervisor(s):
Yury Korolev and Matthias Ehrhardt

Project description:
In inverse problems, such as computed tomography (CT) and photoacoustic tomography (PAT), the goal is to reconstruct a quantity of interest from indirect and often noisy measurements. These problems are challenging due to their ill-posed nature and the complexity of the data. Traditional approaches such as variational regularisation, have been effective, but are often limited by their handcrafted design (e.g. promoting smoothness in the reconstruction), which can be restrictive and insufficiently adaptive for capturing diverse reconstruction features. A notable extension is infimal convolution methods, which combines multiple priors to adaptively capture different structural features. However, even these methods can be limited in expressiveness and flexibility.