Personal Statement
I am a Senior Lecturer in Health Data Science. My statistical research interests include computation methods for approximate Bayesian analysis using Variational Bayes, methods for longitudinal data, and prediction modelling. I work with a wide range of clinical collaborators across a range of disciplines including cancer research, diabetes, epilepsy, rheumatology and mental health. I'm interested in analysis that utilises large electronic health records such as the Clinical Practice Research Datalink (CPRD).

I am part of the Civic Health Innovation Lab (CHIL; https://www.liverpool.ac.uk/civic-health-innovation-labs/) using large electronic health records from the region to answer important public health questions. I'm part of a team working with Liverpool City Region Civic Data co-operative, aiming to use advanced data science, statistical and computational methods to answer complex health questions, and implement digital health solutions at scale across Cheshire and Merseyside.

I'm always interested to hear from potential PhD students who are interested in pursuing a PhD with me in the areas of longitudinal/spatial data modelling, Bayesian computation methods or risk prediction.

Research Overview
I am interested in a Bayesian technique called Variational Bayes. This is a faster alternative to the usual Markov Chain Monte Carlo (MCMC) methods used but is sometimes less accurate. My work involves deriving variational algorithms to use in a variety of health related applications, usually revolving around longitudinal data or risk prediction.

I'm interested in supervising PhD students in areas of Bayesian computation, longitudinal modelling or risk prediction.

I contributed to the analysis of COVID data for a number of projects, including analysis of in-hospital patients in the ISARIC cohort, and the assessment of SMART testing policies in collaboration with the CIPHA platform.

I previously completed a UKRI Innovation Fellowship funded by the MRC, investigating variational approximations in longitudinal data analysis. My research involves approximate computationally efficient methods of estimating mixed models for multivariate longitudinal data. I'm also working on jointly modelling longitudinal and survival data. Most of this work is focused on clinical risk prediction methods, and covers applications involving diabetes, epilepsy and liver cancer.

My postdoc research involved analysing longitudinal data and developing methods to correctly account for the correlation between biomarkers of various types. My main work is in methods of discriminant analysis to classify patients into disease risk groups. This was part of the MRC funded DiALog project.

Prior to this I completed my Ph.D in the Department of Mathematical Sciences. My PhD work was in the area of nonparametric regression and involved applying monotonicity constraints to regression estimates in psychometric studies.