  Episodic mass loss is not understood theoretically, neither accounted for in
state-of-the-art models of stellar evolution, which has far-reaching
consequences for many areas of astronomy. We introduce the ERC-funded ASSESS
project (2018-2024), which aims to determine whether episodic mass loss is a
dominant process in the evolution of the most massive stars, by conducting the
first extensive, multi-wavelength survey of evolved massive stars in the nearby
Universe. It hinges on the fact that mass-losing stars form dust and are bright
in the mid-infrared. We aim to derive physical parameters of $\sim$1000 dusty,
evolved massive stars in $\sim$25 nearby galaxies and estimate the amount of
ejected mass, which will constrain evolutionary models, and quantify the
duration and frequency of episodic mass loss as a function of metallicity. The
approach involves applying machine-learning algorithms to select dusty,
luminous targets from existing multi-band photometry of nearby galaxies. We
present the first results of the project, including the machine-learning
methodology for target selection and results from our spectroscopic
observations so far. The emerging trend for the ubiquity of episodic mass loss,
if confirmed, will be key to understanding the explosive early Universe and
will have profound consequences for low-metallicity stars, reionization, and
the chemical evolution of galaxies.
