CV

Supervisor

University of Cambridge

• Natural Sciences, Physics 1A - supervising small teaching groups of undergraduates
• MPhil Data Intensive Science, Statistical Methods - supervising graduate students in statistical theory, probabilistic modelling, and scientific inference for data-intensive applications.
• MPhil Data Intensive Science, Advanced Statistical Methods - supervising graduate students in Bayesian modelling and computation, including Monte Carlo methods, MCMC, nested sampling, and model comparison for scientific data analysis.

PhD Researcher

Cavendish Astrophysics, University of Cambridge

• Research focused on developing advanced Bayesian machine learning pipelines for radio cosmology
• Particularly the detection of the 21cm signal redshifted from the Cosmic Dawn and Epoch of Reionisation
• Supervised by Dr Eloy de Lera Acedo

Graduate Researcher

Institute of Astronomy, University of Cambridge

• Research focused on optimisation of probabilistic unsupervised machine learning methods for near-field cosmology
• Indentifying stellar subpopulations from within chemo-dynamical datasets constructed from GAIA, APOGEE and GALAH
• Enabling robust, uncertainty-aware insights into the formation and assembly history of the Milky Way
• Building pipelines designed for the volume and dimensionality offered by the next generation of surveys such as WEAVE and 4MOST

PhD Astrophysics

University of Cambridge, UK

MPhil Data Intensive Science

University of Cambridge, UK

Graduate course jointly taught across DAMTP, the Institute of Astronomy, and the Cavendish Laboratory, covering statistical inference, deep learning, and advanced software development for large-scale astronomical data. Focusing on applications to theoretical astrophysics including galactic archaeology, cosmology, and gravitational waves.

• Grade: High Distinction (85%)

BSc Physics

Durham University

Took modules in pure mathematics, theoretical physics, and astrophysics, complemented by research projects in computational modelling, statistical inference, and observational techniques conducted within the Centre for Extragalactic Astronomy and the Institute for Computational Cosmology.

• Grade: First Class (4th in Cohort)

A-Level

Churcher’s College

Mathematics, Further Mathematics, Physics, Chemistry

• Grades: 4*s

UKRI Travel Grant

UKRI Digital Research Infrastructure (DRI)

Funding to present work on GPU-accelerated Bayesian inference for SKA-era 21-cm cosmology at the AI in Radio Astronomy Workshop, Royal Observatory Edinburgh.

Jesus College Scholarship

Jesus College, University of Cambridge

Recognised for outstanding academic achievement in the MPhil.

Cavendish Scholarship

Cavendish Laboratory, University of Cambridge

Selected for the prestigious Cavendish Scholarship Programme.

Harding Distinguished Postgraduate Scholar

University of Cambridge

Selected for the Harding Distinguished Postgraduate Scholars Programme.

Durham Physics Award

Department of Physics, Durham University

Awarded for Outstanding First-Class Achievement in BSc Physics.