Unexpected Insights from Astronomical Data with Stephanie Juneau (NSF NOIRLab, CosmicAI)

Описание: CosmicAI Spring Hybrid Seminar Series
From Astronomical Data to Knowledge: A Few Unexpected Insights from Machine Learning
Presenter: Stephanie Juneau

Abstract: 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 Bio: Dr. Stéphanie Juneau is an Associate Astronomer at NSF NOIRLab whose work focuses on galaxy evolution, supermassive black holes, and the role of feedback in regulating star formation. She obtained an Astronomy PhD from the University of Arizona, has extensive experience working with large spectroscopic and multi-wavelength surveys, and her recent efforts explore the use of machine learning and representation learning to extract physical insight from complex astronomical datasets. She is actively involved in the NSF–Simons CosmicAI Institute as a science lead, contributing to research case studies and the development of scalable, AI-enabled data platforms for astronomy.