My research focused on Bayesian data analysis, stochastic modelling, and computational statistics. In my PhD I developed statistical and simulation-based models to study the progression of mitochondrial disease, combining Bayesian inference with stochastic models of mtDNA population dynamics.
This work involved building agent-based simulations, fitting complex probabilistic models to both real-world and synthetic datasets, and investigating the use of advanced computing hardware to accelerate Bayesian inference.
This blog showcases projects and articles related to statistical modelling, Bayesian methods, stochastic processes, and high-performance statistical computing. The aim is to demonstrate practical approaches to modelling complex systems and analysing data using modern statistical tools.
I also share additional projects in statistics, data science, and computational modelling which I am interested in.