Computational modeller. I build mathematical models and large-scale numerical simulations of fluid and particulate systems, and I care about the numerics underneath — solver behaviour, stability, and whether a result can be trusted.
PhD in Applied and Computational Mathematics (Monash), currently a research scientist at the University of Newcastle working on data-driven CFD and ensemble forecasting. Based in Newcastle, NSW. Australian citizen.
Numerical methods for nonlinear PDEs. I derived the Eigenenergy Decomposition Method, which resolves a long-standing open problem in magnetohydrodynamics: the exact analytical decomposition of nonlinear, fully coupled wave energies in magnetised fluids. Published as a three-paper series in The Astrophysical Journal (2024–2025), with the reference implementation released here.
Simulation at scale. Extending established CFD codes (LaRe3D, Fortran/MPI) to ingest observational data as time-dependent boundary conditions, and scaling simulation and mpi-parallelised post-processing.
Applied modelling for industry. DEM calibration of stockpile formation in bulk material transport for Bechtel, and modelling and optimisation of an industrial slurry dewatering pipeline for Jord — both delivered through the University of Newcastle.
| AutoParallelizePy | A Python library extending mpi4py that automates domain decomposition and non-contiguous MPI parallelisation of multidimensional datasets. Ships with an installer, API documentation, and worked examples. |
| EEDM | MPI-parallelised solver and parser implementing the eigenenergy decomposition on large multidimensional simulation datasets. The computational pipeline behind the ApJ series. |
- PhD, Applied and Computational Mathematics — Monash University
- Peer-reviewed publications in The Astrophysical Journal, MNRAS, Solar Physics, and the ANZIAM Journal — Google Scholar · ORCID
- Fortran · Python · C++ · MPI · HPC · Linux
- CFD · DEM · finite-volume and finite-difference methods · model calibration and validation · uncertainty quantification · ensemble forecasting · machine learning
Keen to work on industry projects: simulation, modelling and data-driven in mining, water, energy and infrastructure.