About Me

I am currently at Princeton University studying ways to advance science through the use of machine 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 previously worked as a data scientist/computational statistician in fields of information geometry and biology.

Currently: PhD student Princeton University
2021: Honours in Astrophysics Australian National University
Major: Computational astrophysics
2020: Bachelor of Mathematics Queensland University of Technology
Major: Applied and computational
2020: Bachelor of Science Queensland University of Technology
Major: Physics

Research Interests:
Machine Learning
Computational Statistics
Computational Astrophysics
Cosmic Ray Transport

Google Scholar


Score-based diffusion models
Currently working on a neural network that utalises a score based diffusion model to perform source seperation on galaxy images. The model succesfully seperates, then reconstructs an image with individually rendered galaxies, which may then be analysed.

2nd order uncertainty quantification
Neural networks have shown significant promise in helping to solve inverse problems in science, specifically in the upscaling/inpainting of images in low resolution datasets. However, hallucinations are an unwanted artifact in these reconstructed images. I am working with my supervisor on a computationally efficient 2nd order method for quantifying the uncertainty of image features being halluninations.

Cosmic ray diffusion
I also work on simulating cosmic ray propagation, specifically on larger scales where we do not resolve the cosmic ray gyroradius. I assisted in the development of a propagation code CRIPTIC with my former supervisors Mark Krumholz and Roland Crocker.


I am passionate about teaching at all levels, from beginner to advanced material in physics, mathematics and computer science. Throughout my studies I have always been actively involved in teaching both undergraduate and graduate students, specifically in the fields of mathematics and statistics.

Selected courses taught:
Statistics for biology
Physics of the very large
Quantatative analysis
Experimental physics


I'm always happy to talk about astrophysics, machine learning and really anything science related.

Email: matt.sampson@princeton.edu
Address: Peyton Hall
4 Ivy Lane, Princeton

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