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Explore the intricacies of star formation histories in galaxies both nearby and distant in this 25-minute conference talk by Eric Gawiser from Rutgers University. Delve into the application of astrostatistics and machine learning tools to galaxy formation and evolution. Discover how Integral Field Unit surveys are revolutionizing our understanding by producing hundreds of spectra per galaxy across tens of thousands of galaxies. Learn about the wealth of information contained in galaxy morphology through imaging data, down to the pixel level and across various wavelengths. Examine how statistical and machine learning-powered outlier detection algorithms are uncovering anomalous galaxies that challenge current paradigms, and how this trend will accelerate with upcoming projects like Rubin, DESI, Roman, Euclid, and the SKA. Gain insights into the crucial role of data science tools in linking observations with theoretical models, including cosmological hydrodynamical simulations and semi-analytic or empirical models built on dark matter-only simulations.
