About me

I grew up in the south of France where I studied under the French educational system. Holding French and British nationalities, I grew up speaking both languages. After obtaining my French Baccalaureat in 2016, I joined the University of Glasgow on a Joint BSc in Statistics and Finance before transferring to an MSci in Statistics with Work Placement in 2018.

I graduated in 2021 from the University of Glasgow and throughout my degree, I developed a particular interest for both time series and machine learning. My undergraduate dissertation focused on building forecasting models of A&E attendances in Scottish hospitals. For my master’s, I worked on applying Bayesian changepoint detection to coal mining disasters data.

My interest in research began during my work placement with NHS Scotland. While working as an information analyst within my team, I additionally conducted independent research on forecasting A&E attendances (which served as my dissertation topic). This greatly improved my ability to extract important information from research papers and apply concepts and ideas to my own work.

I am passionate about solving real world problems and thus decided to join the STOR-i programme at Lancaster University, as I believe it offers the best opportunity for me to further develop the skills necessary to successfully work on research in industry. 

My PhD

Since the late 19th century, the average surface temperature has risen by 1.04°C. The exponential increase of gas emissions is in part responsible for Earth’s global warming. Today, we emit around 50 billion tonnes of greenhouse gases each year, with the majority produced by the burning of fossil fuels, industrial production, and land use change. Methane can be released during oil and gas extraction, this is often referred as “fugitive emissions”. The short lifetime of methane implies that reductions in its emissions rapidly results in lowering its concentration in the atmosphere. Hence, tackling methane emissions could be an effective and rapid way to mitigate some of the impacts of climate change.

My research focuses on locating source(s) and quantifying emission rate(s) of anthropogenic greenhouse gases; with a focus on methane. To do so, I am modelling gas dispersion in the atmosphere and implementing probabilistic inversion for source characterisation.

Gas dispersion/Forward models: I am predicting spatio-temporal gas dispersion using different forward models and assessing their computational cost and accuracy under different atmospheric conditions. The forward models currently used are Gaussian plumes and discretised advection-diffusion equation using finite volume methods on a Navier-Stokes flow field. The later being a more computationally expensive but physically-realistic forward models from computational fluid dynamics.

Probabilistic inversion for source characterisation: Additionally, I develop novel methodologies involving gradient-based MCMC algorithms, Kalman filters and particle filters to perform efficient probabilistic inversion, which identifies source(s) location based on gas concentration measurements. Due to the high-dimensional nature of the problem, inversion is computationally expensive. Hence, this research is undertaken with the aim to create models which are computationally fast and applicable in the real world. In practice, fast computational inversion models can be used for live tracking of emissions by drones or satellites.

Stochasticity is an overarching problem in this research as there are many sources of randomness. For example, methane sensor measurement error, approximation of gas dispersion and spatial estimation of methane background concentration must be accounted for in order to correctly located and quantify emissions. These uncertainties are quantified using probabilistic numerics as they must be propagated across from computations in the forward modelling to the inversion modelling.


Here is a summary of my degree qualifications and where I attained them.


This page contains the research that I have conducted.