Research

My PhD project involves applying cutting-edge machine learning to big data astrophysics. I 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 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.

I am working on a project aiming to incorporate high resolution spectroscopic measurements with photometric measurements in our supernovae inference. This has lead me to draw inspiration and work on other hierarchical Bayesian models for white dwarf calibration. Another one of my projects involves using simulation based inference for Type Ia model selection where we use Bayesian evidence to select the best model. I am also looking at using simulation based inference to account for Type Ia survey selection bias and compare results to traditional correction techniques. 

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. A project I did with the CDT involved implementing a Wordle solver using information theory! 

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

Awards and Prizes

2023          G-Research Grant for Early Career Researchers