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The building blocks of the known Universe are stars. To study what governs the structure and evolution of our galaxy, the Milky Way (MW), its companion Andromeda (M31), or the Local Group (LG) dwarf galaxies, means to observe and understand the processes that govern the stellar birth, evolution, death, and motion over their evolutionary time-scales. The collection of data on our Galaxy represents the first step in this work of archeological research, and surveys such as Gaia, SDSS, APOGEE2, RAVE as well as forthcoming missions and telescopes such as SDSS-V, LSST, JWST, GMT, TMT will project us into a golden age of the MW & LG research. Exploiting datasets of this scope is not merely a case of applying the same tools that have been used historically: it demands the development of, and experience with, leading-edge multi-dimensional machine learning techniques for data mining. I will review the open questions in this research field and introduce a novel population synthesis model developed to face the theoretical challenges of this new era.

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