Time-domain space missions have revolutionized our understanding of stellar physics and stellar populations. Virtually all evolved stars can be detected as oscillators in missions such as Kepler, K2, TESS and PLATO.  Asteroseismology, or the study of stellar oscillations, can be combined with spectroscopy to infer masses, radii and ages for very large samples of stars.  This asteroseismic data can also be used to train machine learning tools to infer ages for even larger stellar population studies, sampling a large fraction of the volume of the Milky Way galaxy. In this talk I demonstrate that asteroseismic radii are in excellent agreement with those inferred using Gaia and spectroscopic data; this demonstrates that the current asteroseismic data is precise and accurate at the 1-2% level.  Major new catalogs for Kepler and K2 data are nearing completion, and I present initial results from both. We find unexpected age patterns in stars though to be chemically old, illustrating the power of age information for Galactic archeology.  Prospects for future progress in the TESS era will also be discussed.