4 West 1.17
Department of Mathematical Sciences
University of Bath
Bath, BA2 7AY
United Kingdom
I graduated from the University of Bath in summer 2020 with a first-class honours Master of Mathematics. Currently I am doing an Integrated PhD in Statistical Applied Mathematics at the University of Bath, as part of the 7th cohort of the EPSRC SAMBa CDT. Having finished the MRes year, I am now in my second year of PhD research.
My undergraduate Masters dissertation on "efficient priorconditioning for edge enhancement in imaging" was supervised by Dr Silvia Gazzola and Professor Alastair Spence. The project involved the regularization of discrete ill-posed linear inverse problems and employing Krylov subspace methods that adaptively define edge-enhancing encodings between iterates.
Now, my PhD topic on "learned regularisation for inverse problems" concerns how one can use machine learning techniques when solving inverse problems. It is being supervised by Dr Matthias J. Ehrhardt and Dr Silvia Gazzola.
My mathematical interests primarily lie in numerical analysis, with inverse problems being a particular focus. Inverse problems arise naturally in various applications such as medical imaging, wherin one has a quantity of interest (e.g. brain scan) but only has access to an indirect measurement (e.g. sinogram/output of a medical device) and an understanding of how these quantities are related (e.g. radon transform). The task is then: given this indirect measurement, what is associated quantity of interest that gives rise to said measurement?
Due to noise in the measurement data, directly solving this problem often leads to a meaningless reconstruction of the quantity of interest. Instead, one considers a "nearby" problem that is less sensitive to noise, but still representative of the orginal problem - a technqiue called variational regularisation. How "nearby" you are is controlled by a non-negative number called the regularisation parameter. My PhD concerns how one can use machine learning techniques, with mathematical guarantees, to solve variationally regularised problems, with a focus determining the choice of the regularisation parameter.
During my MRes year of the Integrated PhD, I took the following modules:
During my first year of research, the following taught modules were also taken:
I have led tutorials for various modules at the University of Bath including:
WHERE TO FIND ME
You can find and get in touch with me on a variety of platforms which include but are not limited to
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