Eric G. M. Muller

PhD Candidate

About Me

Hi! I’m Eric, a PhD candidate at The Australian National University, researching machine learning methods for predicting cold gas fractions from HI emission spectra, under Dr. Hiep Nguyen. I’m particularly interested in machine learning methods for quantifying the uncertainties on predictions and using these for real science. In 2023 I finished my Honours in astrophysics at the ANU, where I worked on characterising the spatial variations of dust in galaxies from the MAGPI and SAMI surveys, under Dr. Andrew Battisti.

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Interests

Machine Learning
Radio Astronomy
Spatial distributions of dust in galaxies

Education

PhD Candidate - Machine Learning Methods for Cold HI
The Australian National University
BSc (Hons) - Astronomy and Astrophysics
The Australian National University

Current Projects

Python

Machine Learning for HI Fraction Prediction

A project using Bayesian Physics-Informed Neural Networks to predict the CNM, UNM, and WNM fractions of HI spectra. Specifically looking at a hybrid CNN-Transformer architecture with Bayesian CNN weights and biases for stochastic predictions to provide error bounds on predicted values. Currently refining the architecture and investigating skip connections, as well as the need for Bayesian Transformer weights.

Feb 1, 2024

Python

Dust Attenuation in SAMI & MAGPI

A project looking at the correlations between dust attenuation and galaxy properties (stellar mass, star formation rate, and gas-phase metallicity) in SAMI and MAGPI galaxies, on both spatially-resolved and integrated scales.

Jan 30, 2023

Recent Publications

2025

Caroline Foster, Mark W. Donoghoe, Andrew Battisti, et al. The MAGPI Survey: the kinematic morphology-density relation (or lack thereof) and the Hubble sequence at z ∼ 0.3 Publications of the Astronomical Society of Australia, accepted for publication, February 2025
arxiv
Marcie Mun, Emily Wisnioski, Katherine E. Harborne, et al. The MAGPI Survey: radial trends in star formation across different cosmological simulations in comparison with observations at z ∼ 0.3 Monthly Notices of the Royal Astronomical Society, accepted for publication, February 2025
arxiv MNRAS
Hiep Nguyen, Haiyang Tang, Matthew Alger, Antoine Marchal, Eric G. M. Muller, Cheng Soon Ong, N. M. McClure-Griffiths. TPCNet: representation learning for HI mapping Monthly Notices of the Royal Astronomical Society, Volume 536, Issue 1, January 2025, Pages 962-987
arxiv MNRAS

Skills

General

Python

Data Science

bash

Machine Learning

PyTorch

Scikit-Learn

tensorflow

pyro

Astronomy

TOPCAT

Awards