Research

My PhD project will involve applying cutting-edge machine learning to big data astronomy. I will work on Bayesian models for Type Ia supernovae, supervised by Dr Kaisey Mandel. Type Ia supernovae are standard candles meaning if we have a model for how bright they are, we can determine their distance. These distances can be combined with galaxy redshifts to put constraints on the expansion and age of the Universe using Hubble's Law. The models are perfect for machine learning as they vary in time as well as wavelength, meaning they are high-dimensional. Taking a hierarchical Bayesian approach to this problem allows us to make inferences on other physics models such as dust extinction laws.

Qualifications

2022-       PhD Astrophysics (CDT Data Intensive Science) University of Cambridge

2021-2022 MSc Advanced Computing (Machine Learning) Imperial College London

2017-2021 MPhys Physics Durham University