Alex Baker

I am an undergraduate physics student at Université Laval, working at the intersection of astrophysics, machine learning, and scientific computing. My current work focuses on developing interpretable, physically grounded pipelines for astronomical data, with a particular interest in stellar spectroscopy, galaxy morphology, and cosmological inference.

My path to astrophysics was not a straight line. For several years, I explored different directions before a deeper curiosity about the Universe, its structure, and the mathematical language behind physical phenomena gradually pulled me toward physics. Today, this curiosity shapes the way I approach research: I am especially interested in using computational tools not only to make predictions, but to reveal physical structure in complex datasets.

Research interests: stellar spectral classification, interpretable machine learning, astronomical surveys, chemodynamics of stellar populations, galaxy morphology, dimensionality reduction, Bayesian inference, dark energy models, and self-supervised representation learning for astrophysical data.

My current projects include:

• AstroSpectro — An interpretable machine learning pipeline for stellar spectral analysis using LAMOST DR5 and Gaia DR3 data. The project combines physics-based feature extraction, supervised classification, dimensionality reduction, and SHAP interpretability to study how spectral lines, metallicity-sensitive features, and stellar parameters structure the observed spectral space.

• AstroVision — A deep-learning project for galaxy morphological classification. The pipeline combines visual foundation-model features, non-parametric morphometrics, photometric information, and uncertainty estimation methods to explore galaxy structure in large imaging surveys.

• ξ Dark Energy · BAO MCMC — An exploratory Bayesian analysis comparing ΛCDM, CPL, and oscillating dark energy models using Pantheon+SH0ES supernovae and DESI DR2 BAO data. The project focuses on reproducible MCMC workflows, model comparison, and cosmological parameter inference.

I am currently preparing for graduate studies in computational astrophysics and am especially interested in research opportunities involving large astronomical surveys, interpretable machine learning, astrostatistics, and physics-driven data analysis. I am always happy to connect with researchers, students, and collaborators working at the interface of astrophysics and modern computational methods.

“The most beautiful thing we can experience is the mysterious. It is the source of all true art and science.”
— Albert Einstein