Clinical Data Scientist / Forward Deployment Engineer - PhD graduate in Biomedical AI.
Research focus: haemodynamic modelling with thermoregulation, and deep learning for blood flow prediction. Thesis work involved generating 0D models with thermoregulation, building a 1.44 million virtual patient database for 1D arterial simulation, and designing a novel deep residual graph CNN for haemodynamic metric prediction. Since that hadn't been done before, I now have extensive experience with architectures that don't work for this problem.
Projects: tinytorchtest (PyTorch sanity-check testing), Hazen (MRI QA tooling - lead contributor), Haemflow-cfd (0D haemodynamic modelling with thermoregulation and pareto-front parameter fitting from patient measurements).
- Primary tools: Python, Fortran, R, Emacs Lisp.
- Development environment: The Church of Emacs.
- Bootloader: Gentoo Linux.
- Hobby tools: Forth, Common Lisp, Rust.
- Class: Social Justice Mage.
- Bread: Bread.




