I am a Mathematical Innovation Research Associate at the Institute of Mathematical Innovation at the University of Bath. My main focus is on computer vision methods used to detect damage in PsA x-rays and I work closely with the Computer Science department and the CAMERA group.
I was previously (2020-2021) a Research Associate in the Department of Pharmacy and Pharmacology at the University of Bath, working with Dr William Tillett, on the use of Machine Learning in Psoriatic Arthritis (PsA) Diagnosis.
I completed my PhD in the Statistical Applied Mathematics CDT at Bath (SAMBa) in 2020. Before this I completed my undergraduate degree at the University of Warwick, obtaining a Master in Mathematics, Operations Research, Statistics and Economics (MMORSE).
Broadly speaking, my research interests are in Machine Learning, Image analysis, Computer vision and their applications to health sciences. I work with the Royal United Hospital (RUH) in Bath on trying to trying to find synergies between the hospital and the University of Bath to try answer critical clinical questions.
I am interested in Gaussian Processes or Gaussian Fields. You can find a small introduction and overview on them here: Gaussian Processes
I study in Statistical Shape Models(Page Under construction) and how they can be used to track change over time of bone and joint structures in the context of PsA damage.
MALARD stands for MAchine LeARning for Rheumatic Diseases. This is a collaboration between the Royal United Hospital and the University of Bath departments of Pharmacy and Pharmacology, Computer Sciences and Mathematical Sciences with the aim of automating damage detectiong in PsA radiographs. More information can be found on
This is a collaboration between the Royal United Hospital, Heriot-Watt University and the University of Bath. We seek to perform uncertainty quantification on artefacts appearing in lung CT images. The ultimate aim is to be able to perform rigorous statistical testing of artefacts to make a more informed decision on the nature of the observed artefacts. This project is led by Audrey Repetti and Matthias Erhardt and uses advanced optimisation methods to perform hypothesis testing of observed features.
This is an ongoing collaboration between the University of Bath, The National University of Mongolia and various stakeholders from Mongolia. The long term aim of the project is to have a team of Mongolian data scientists and mathematicians who are able to help solve some of the problems faced by Mongolia. Past work has included Workshops in 2016 on Data science and policy, another in 2019 delivered to Government officials and working scientists. The project was pioneered by and is led by Prof Andreas Kyprianou, who has more information on past work. We are also working on a series of short videos to showcase our work and also highlight the issues that could be addressed with mathematical modelling and data science. The video will be in Mongolian and will be aimed at the general population of Mongolia.