About
I am a Research Associate in Statistics at the University of Glasgow. My current work focuses on modelling carbon emissions associated with UK household energy use, in partnership with NatWest Group.
I originally trained in physics, with a particular focus on gravitation and gravitational-wave data analysis. I received the Quantum Fields and Fundamental Forces MSc from Imperial College London in 2017, and completed my PhD in Gravitational Wave Data Analysis at the University of Glasgow in 2022. My PhD work combined Bayesian inference, gravitational-wave modelling and efficient likelihood computation, and was awarded the Kelvin Award.
Since joining the School of Mathematics and Statistics in 2024, my work has broadened into applied statistical modelling of household energy use and residential emissions. I use household-level consumption, property and emissions data to assess how accurately domestic carbon emissions can be estimated under different levels of data availability.
Research interests
My current research focuses on statistical modelling of household energy use and carbon emissions, particularly in the context of residential mortgage portfolios and financed-emissions reporting.
In this project, I work with linked household energy and property data — including smart-meter gas and electricity consumption, Energy Performance Certificate data, dwelling characteristics and national electricity carbon-intensity data — to estimate annual energy-related household emissions. A central aim is to understand how predictive accuracy changes under different levels of data availability, and what this implies for data-quality frameworks such as the PCAF data-quality score hierarchy.
Methodologically, this work involves data curation, harmonisation and integration, regression modelling, additive models, imputation, model selection, cross-validation, and out-of-sample predictive-error analysis.
Although my current applied work focuses on household energy and emissions modelling, my broader statistical background is in Bayesian inference and gravitational-wave parameter estimation. My PhD work developed computationally efficient likelihood and downsampling methods for long-duration gravitational-wave signals, particularly in the context of the future LISA mission. This background continues to shape my interest in uncertainty quantification, likelihood-based inference and efficient statistical computation.
More broadly, I am interested in Lorentzian geometry, general relativity and variational principles, particularly in the context of Lorentzian manifolds. I also have interests in information geometry, gravitational-wave astronomy and data analysis, and the foundations of spacetime physics. Outside mathematics and statistics, I have a strong interest in music and composition, and hold a BMus degree in composition from the Royal Academy of Music.