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Mitchell Cavanagh

ML Engineer | Astrophysics PhD

Welcome to my website!

Here you’ll find me (occasionally) writing about various topics I find interesting. Some featured posts include:

The barred-spiral galaxy NGC 2217 (image credit: Wikipedia/ESO)
Predicted spiral / bar masks from my galaxy segmentation model.

or, browse by topic:

algorithms astronomy complex networks cosmology darts differential equations fortran gaming GAN geometry keras linguistics longform machine learning math n-grams neural networks nlp optimisation optuna parallelism planetary science programming python pytorch quantum shpc transformer umap university vae visualisation vqvae worldbuilding

My Background

I’m an ML software engineer at DUG Technology, where I’ve helped develop and maintain a custom config-driven PyTorch framework for on-premises distributed training, integrate LLM chat requests, and train and deploy AI models for seismic data processing.

I completed my PhD in Astrophysics at ICRAR/UWA in 2023. I’ve published five first-author papers applying AI models to study the morphological evolution of galaxies, including:

  • developing (from scratch) several CNNs for galaxy classification
  • creating one of the first U-Nets for segmenting spiral arms and galaxy bars into probability masks, and
  • designing a transfer learning pipeline for improved high-redshift galaxy classification.

These models have been applied to over 100,000 galaxies across observational datasets (e.g. SDSS, COSMOS, SAMI), as well as synthetic galaxy images from cosmological simulations (EAGLE).

Preprints of my first-author papers are available on arXiv.
My PhD thesis is publicly available on UWA’s research repository.
My work was featured in an interview with Cosmos Magazine!
See my ORCID and/or Google Scholar profile for my full list of publications.