I am currently at Princeton University studying ways to advance science through the use of machine learning with a particular focus on modelling dynamical systems and geometric deep learning. Previously I was at the Australian National University where I performed numerical experiments and helped develop a code to simulate cosmic ray propagation through turbulent plasma. I have also worked as a data scientist/computational statistician in fields of information geometry and biology.

Interests

- machine learning
- deep learning
- neural/latent ODEs
- graph neural networks
- computational statistics
- astrophysics

Education

PhD astrophysics and machine learning

Princeton University

MA Astrophysics and Machine Learning

Princeton University

BHons Computational Astrohysics

Australian National University

BSc Computational Mathematics

Queensland University of Technology

BSc Physics

Queensland University of Technology

recent publications

Path-minimizing Latent ODEs for improved extrapolation and inference.
*MLST (full version) NeurIPS Workshop for Physical Sciences*.

(2024).
Disentangling transients and their host galaxies with Scarlet2: A framework to forward model multi-epoch imaging.
*arXiv preprint arXiv:2409.15427*.

(2024).
Score-matching neural networks for improved multi-band source separation. Astronomy and Computing.
*Astronomy and Computing*.

(2024).
Spotting Hallucinations in Inverse Problems with Data-Driven Priors.
**Spotlight talk** *at Machine Learning for Astrophysics. Workshop at the Fortieth International Conference on Machine Learning (ICML 2023), July 29th, Hawaii, USA*.

(2023).
Turbulent diffusion of streaming cosmic rays in compressible, partially ionized plasma.
*Monthly Notices of the Royal Astronomical Society 519 (1), 1503-1525*.

(2023).
Cosmic ray interstellar propagation tool using Itô Calculus (criptic): software for simultaneous calculation of cosmic ray transport and observational signatures.
*Monthly Notices of the Royal Astronomical Society 517 (1), 1355-1380*.

(2022).