Understanding physical processes responsible for the formation and evolution of galaxies like the Milky Way is a fundamental but unsolved problem in astrophysics. Most stars are long-lived, using the stars as “fossil records” (what is known as Galactic archaeology) can offer unparalleled insight into the assembly of galaxies. The landscape of Galactic archaeology is rapidly changing thanks to on-going large-scale surveys (astrometry, photometry, spectroscopy, and asteroseismology) which provide a few orders of magnitude more stars than before. I will discuss various “phenomenological” opportunities enabled by large surveys. I will also discuss how we could describe substructures in the Milky Way through the lens of deep learning, and how deep learning could maximally harness information from stellar observables.
