I am a PhD student researching machine learning and Bayesian inference applications to Type Ia supernova cosmology and DA white dwarf calibration, under the supervision of Prof. Kaisey Mandel. Type Ia supernovae are standardisable candles meaning if we have a model for how bright they are, we can estimate their distance. These distances can be used to put constraints on the age, expansion and dark energy density of the Universe. I am working on using simulation-based inference to account for selection biases in supernova cosmology. Another one of my projects involves developing a hierarchical Bayesian technique to model high resolution spectra along with photometric measurements for DA white dwarf calibration.
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!
Research Interests: Machine Learning, Cosmology, Type Ia Supernovae, Bayesian Inference, Astrostatistics
First author papers:
DAmodel: hierarchical Bayesian modelling of DA white dwarfs for spectrophotometric calibration
https://academic.oup.com/mnras/article/540/1/385/8114814
Accounting for Selection Effects in Supernova Cosmology with Simulation-Based Inference and Hierarchical Bayesian Modelling
https://arxiv.org/abs/2407.15923
