Modern astronomy is increasingly defined by the scale and complexity of its data, with large surveys producing measurements for millions to billions of objects across space, wavelength, and time. In this talk, I will provide a broad overview of contemporary astronomical data, with an emphasis on large datasets that are particularly well-suited for machine learning and AI-driven analysis. I will briefly outline emerging ideas for the CosmicAI Data Platform, focusing on data curation, access patterns, and data services designed to support modern ML workflows. I will then highlight examples from optical spectroscopic surveys such as SDSS and DESI to illustrate how unsupervised approaches can uncover structure in complex spectroscopic data beyond what is typically accessed via traditional analyses. I will conclude by briefly discussing how these results motivate future work on scalable, interpretable, and multimodal approaches to astronomical data analysis.

Speaker: Stéphanie Juneau is an Associate Astronomer at NSF’s NOIRLab, as well as a Survey Data Scientist in the Astro Data Lab team. Her research interests focus on galaxy evolution, and she is especially intrigued by how galaxies formed and assembled their stars, as well as by the interplay between star formation and active accretion around supermassive black holes at the centers of galaxies — or Active Galactic Nuclei (AGNs). She has authored and co-authored 169 publications, 89 of which are peer reviewed.