Galaxy formation and evolution is a field that combines many complex and interdependent physical processes across different spatial and time scales. Moreover, observational data exists across a large wavelength range, and with heterogeneous spatial resolutions and observing depths, each sensitive to different physical tracers. Astrostatistics and machine learning can help us mine the huge existing and upcoming multiwavelength datasets, effectively down to the pixel and spaxel level, in order to determine the fundamental relationships between galaxy properties and evolutionary processes. Additionally, novel techniques are required to facilitate comparisons between galaxy formation theories and all observational data — including outlier galaxy populations, and to integrate galaxy formation physics in the inference of cosmological parameters. However, connecting statistics and machine learning results to an improved understanding of key astrophysical processes remains a major challenge. This program will initiate a discussion between the empirical exploration of the extremely large and diverse data sets from both observations and simulations and the deduction of the underlying physics, aiming to answer the question: How do we optimally combine the empirical intuition gained using data science tools with theoretical intuition and knowledge, with the deductive goal of understanding the physical processes that govern the formation and evolution of galaxies?
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