I graduated with a BSc in Computer Science from Newcastle University in 2019, joining the CDT in the same year. My dissertation focussed on applying machine learning techniques to create a chess AI, optimised for low-end hardware.
The CDT appealed to me as it included a taught year to bring us all up to speed on the state of statistics and machine learning, before offering a wide range of projects to take for our research. The opportunity to apply our knowledge in diverse fields really appealed to me, as collaboration between disciplines is core to modern science.
In my spare time, I write stand up comedy and listen to heavy metal.
PhD Title
Creating mock galaxy catalogues using deep learning
My research takes dark matter simulations and aims to seed these simulations with populations of galaxies, precisely predicting their properties. The Intrinsic Alignment (IA) signal of these galaxies is of particular interest, as it is a significant generator of noise when trying to take accurate measurements of dark matter and dark energy. To do this is, I use Graph-Generative Adversarial Networks. A huge obstacle for this project will be accounting for the scale of the graphs I am creating, as they will be orders of magnitude larger than the graphs used in most applications. This project is being undertaken in collaboration with the School of Maths, Stats and Physics, as well as the LSST Dark Energy Science Collaboration.
