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Kavli Institute for Cosmology, Cambridge


I am a PhD student researching machine learning and Bayesian inference applications to type Ia supernova cosmology, under the supervision of Prof. 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 used to put constraints on the age, expansion and dark energy density of the Universe. 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.

Outside of my research group, I am on the Centre Doctoral Training (CDT) in Data Intensive Science. The CDT provides me with several courses that allow me to keep up to date with the latest machine learning and data science techniques.

Research Interests: machine learning, Bayesian inference, type Ia supernova cosmology, data science, astrostatistics, transient astronomy



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