I am currently a final year PhD student in the Mathematics for Real-World Systems CDT, supervised by Dr Rich Savage (Department of Statistics, University of Warwick), Professor Paul Moss (University Hospital Birmingham) and Dr Keith Roberts (University Hospital Birmingham). My main research interest is machine learning and its applications to problems in medicine, and in particular to problems related to pancreatic cancer.
PhD Project: Bayesian Methods and Data Science with Health Informatics Data
The main focus of my PhD is pancreatic cancer and its risk factors. Pancreatic cancer is the tenth most common cancer in UK with about 8 800 people being diagnosed every year. It is very difficult to detect and diagnose it as it usually does not give rise to any symptoms or signs in the early stages. The survival rate still remains very low despite the immense effort to find out more about the disease: only 1 % of the patients survive 10 years after being diagnosed.
As part of my PhD, I aim to identify novel pancreatic cancer subtypes, which should lead to a personalised and more appropriate treatment of the disease. I am currently working on the development of novel statistical methods for clustering, with a particular focus on the types and sizes of datasets available in the QEHB informatics system. The applications of these methods should hopefully lead to a more comprehensive understanding of the causes of pancreatic cancer and contribute to the development of targeted therapeutics.
- Details of conferences, workshops and summer schools I have attended
- Other academic activities:
- Tutor for ST116 Mathematical Techniques , Oct-Dec 2017
- Organiser of the Machine Learning Reading Group, 2017-2018
- Co-organiser of the tutorials at the MathsSys Summer School: Introduction to Machine Learning, June 2017
- Part of the organising committee for the Centre for Complexity Science Annual Retreat, May 2017
- Part of the editorial team of the Centre for Complexity Science newsletter, 2014-2017
- Research Study Group Project (Using Multi-omic Cancer Data to Find Ways to Improve Treatment of Bladder Cancer) : During the project, we used multi-omic cancer data to improve the treatment of bladder cancer. We analysed the dataset part of The Cancer Genome Atlas (TCGA), in particular focussing on Methylation, Gene Expression and Copy Number Variation data. We found evidence supporting current research on using the gene AQP1 as bladder cancer biomarker. We classified a subgroup of patients with a higher mean age, who had highly methylated genes but better survival prognosis. We also identified ways in which the multi-omic data could be used to provide more detail than the currently used TNM system and performed Multiple Dataset Integration to provide a way of combining the different data types.
- Individual Project (Biomarker-stratified Design of Cancer Clinical Trials) : In this project, I developed an adaptive randomised trial design that enriches the biomarker-positive subpopulation for time-to-event outcomes such as progression-free survival. At the same time, the design addresses the efficacy in the biomarker-negative subgroup, an issue which has been highlighted by the Food and Drug Agency (FDA) in recent years. I tested the design in various settings to establish its clinical relevance and draw conclusions about its effect on the chances of success for the trial
- MSc with Distinction in Mathematics of Systems, University of Warwick, 2014-2015
- BSc with First Class Honours in Mathematics with Management, University of Edinburgh, 2010-2014
- Final year project: Advanced Linear Algebra, supervised by Dr Tom Leinster
- Internship at Lancaster University (2013): dynamic pricing of queues, supervised by Dr Chris Kirkbride
- Internship at University of Edinburgh (2012): simple subgradient methods and their applications for solving huge-scale optimisation problems, supervised by Dr Peter Richtarik
- awarded William and Isabella Dick Prize for excellent performance in the second year, and a vacation scholarship
Office: D2.05 Complexity Science Centre, Zeeman Building, University of Warwick