AstroAI Workshop 2026
Ana Sofía Uzsoy
Manifold learning for cosmic structures
Presenter: Ana Sofía Uzsoy (AstroAI/Harvard University)
Title: Manifold learning for cosmic structures
Date/Time: Wednesday, June 17, 1:30 PM - 2:15 PM
Abstract: We present a scalable manifold learning approach to represent galaxies in a low-dimensional embedding space based on the geometry of their surrounding structure. Our method is a variation on Isomap that calculates similarities between galaxy neighborhood structures and uses multidimensional scaling to preserve them in latent space. We validate this method on a toy dataset consisting of points in balls and lines in space, and demonstrate its utility for astrophysics research on the realistic TNG100 galaxy simulations. For both datasets, our method effectively captures the local structure around each galaxy. For the TNG100 simulations, we show that our first embedding dimension correlates with halo mass and star-formation rate, which aligns with known physical relationships. We also show that we can encode simulation snapshots at different redshifts and align them in latent space, allowing for insight into the evolution of galaxies’ local geometry at different ages of the Universe. Overall, this novel method provides a continuous, scalable, and nuanced way to characterize cosmic structures and is well-suited for applications to large datasets of galaxy positions such as those from DESI and LSST.
Biography: Ana Sofia Uzsoy is a fourth-year PhD candidate in the Astronomy department at Harvard working with Prof. Doug Finkbeiner. Previously, she graduated from North Carolina State University with a B.S. in both Physics and Computer Science, and from the University of Cambridge with an M.Phil in Machine Learning and Machine Intelligence. Her research interests broadly involve galaxy-environment interactions, Lyman-Alpha Emitter galaxies, and applications of statistical and machine learning for extragalactic astrophysics.