Research Interests

My research interests lie mainly in the field of Bayesian statistical inference, particularly Markov chain Monte Carlo (MCMC) methods, data integration and model selection, applied to problems in environmental and biological sciences. My recent research has concerned Bayesian parameter inference and identifiability for stochastic mathematical models of systems biology, specifically related to the NF-κB cell singalling system.

In 2015 I completed my PhD in Statistics at the University of St Andrews (supervised by Professors Steve Buckland and Ruth King), developing methods for the analysis of multi-species ecological data. I then spent two years as a postdoc at the University of Bristol, using Bayesian methodologies for parameter estimation and uncertainty quantification in inverse methods applied to atmospheric chemistry.

• Personal Website

Research Groups

• Environmental Statistics
• Mathematical Biology
• Statistical Methodology
• Statistical Modelling

List by: Type | Date

March 2017 (Co-I): Jean Goulding Institute (Univeristy of Bristol) for data-intensive research seed corn funding (≈£5000) to cover my salary and travel expenses for a project entitled ‘Supervised learning to support the optimisation of chemical reactions’ working with computational chemists at the Universities of Bristol and York and industry partners.

November 2015 (PI): SECURE research workshop grant with additional matched funding from the Cabot Institute (total £2500) for a workshop entitled ‘Modelling uncertainty from multi-scale data streams in environmental and ecological sciences’

Stochastic Processes (Level 4/5)

Multivariate Methods (Level 4/5)

GradStat Fellow of the Royal Statistical Society (RSS)

Associate Fellow of the Higher Education Academy

Meetings Secretary for the Environmental Statistics Section, RSS