Scientific computing

I develop research software that connects physical assumptions to observable predictions. The common pattern is performance-critical modelling in C, with Python used for analysis, visualisation and workflow tooling.

Replace with a schematic of binary-star evolution channels, a workflow diagram, or a figure from a paper (with permission).

binary_c (C)

binary_c is a population synthesis and binary-evolution codebase that I work on and extend. I focus on maintainable performance: careful data structures, clear interfaces, and profiling-led optimisation where it matters.

Implementing and validating physical modules against known benchmarks and observational constraints
Representing orbital phase cleanly so time-dependent physics can be connected to observables
Making provenance explicit (inputs, configuration, outputs) to support reproducible runs

binary_c-python (Python)

I develop Python tooling around the modelling to make it easier to run ensembles, interrogate outputs and build analysis workflows. This includes convenience wrappers, data handling, plotting and sanity checks.

Interfaces that keep the model configuration explicit and versionable
Analysis scripts that turn model grids into publication-ready figures
Lightweight automation (for example, generating derived data products for websites and reports)

Window to the Stars

Window to the Stars is a graphical interface for stellar evolution codes. It has been used in teaching and outreach settings, and it is one of the tools used in Stars for Schools.

Window to the Stars (Stars for Schools)