Here is the page for my interests

Machine Learning

I am extremely interested in machine learning: theoretical developments, in the context of new technologies, but particularly in scientific applications. In any career progression, I am hopeful to work developing machine learning models to solve interesting problems. Of particular interest is geometric machine learning in its many guises: I am curious about the application of normalising flows in high-energy physics contexts, and have developed the HEPGraphs package for interfacing high-energy physics data to the Pytorch-Geometric framework.

Python Development

In my spare time, I like to develop Python packages, to help develop my advanced Python skills and to apply data science diagnostics and visualisation to data-problems applicable to my everyday life.

In a finance context, I have developed starlingpy and costsplit. I use starlingpy to track my finances, categorise transactions and visualise expenditure in an interactive web-app. costsplit is a shameless rip-off (though all original code) of those apps designed to simplify repayments between members of large groups.

In a HEP context, I use my heptools package day-to-day to explore ROOT data and visualise histograms. The hepdash package is a web-based interactive tools for histogram visualisation, built around the streamlit package.

Aerospace

Harking back to my undergraduate days, I have developed AeroJulia, a Julia-lang package which contains many aerodynamic and thermodynamic tools for solving problems in compressible fluid mechanics (sometimes called gas dynamics) contexts.