About

I am a PhD student at the Mullard Space Science Laboratory of University College London, supervised by Prof. Vincent Van Eylen. I am studying through the Centre for Doctoral Training (CDT) in Data Intensive Science, applying Bayesian methods and machine learning to the discovery and characterisation of Exoplanets. I am particularly interested in homogeneous mass characterisation of the small transiting planet population, and the use of multi-dimensional Gaussian Process methods to model correlated noise in timeseries data.

My research focuses particularly on the homogoneous mass characterisation of small transiting exoplanets and on the use of multidimensional Gaussian processes to model correlated noise in time-series data. Missions such as Gaia, Kepler and the Transiting Exoplanet Survey Satellite have produced a wealth of new astronomical data, creating opportunities to uncover new insights into the exoplanet population. However, the increasing volume and complexity of these data also present substantial analytical challenges, particularly as the field prepares for upcoming missions such as Roman and PLATO. My work therefore combines my interests in machine learning, Monte Carlo methods and Bayesian statistics to ensure robust discovery and characterization of both exoplanets and their host stars.

In 2025, I worked as a Data Scientist at The Guardian for my six-month CDT Industry Placement, developing a Natural Language Processing pipeline to automatically suggest relevant keyword tags for news articles, reducing the need for editors to manually search a catalogue of over 25,000 tags. In 2023, I worked as a Support Astronomer at the Isaac Newton Group of Telescopes on La Palma in the Canary Islands of Spain, primarily working with the Intermediate Dispersion Spectrograph and the Wide Field Camera instruments.

Prior to my PhD, I also completed my undergraduate studies at UCL, achieving an MSci in Astrophysics with First-class Honours in 2021. My 3rd-year group project focused on exoplanets, specifically transit timing variations in the orbits of hot Jupiters, while my MSci dissertation focused on astronomical instrumentation and data analysis. I also completed an RAS Summer Internship at MSSL, using recurrent neural networks to predict risks to terrestrial infrastructure, such as power grids and telecommunication networks, from space weather events.