Massive stars drive the radiative and chemical evolution of their host galaxies. Despite the importance of these stars, the physical factors governing their post-main-sequence evolution are poorly understood, and every new observational advance seems to raise more questions than it answers. Beginning now, and continuing through the next two decades, missions like Gaia, TESS, JWST, and the Nancy Grace Roman Space Telescope will observe orders of magnitude more evolved massive stars with unprecedented precision. Simultaneously, cutting edge ground-based instrumentation will allow us to rapidly follow up on new discoveries, driving advances in theory. In this talk, I will touch on work from my PhD thesis that seeks to prepare us for this new era. By borrowing well-understood techniques from the study of low-mass stars, I will show how longstanding problems in the study of massive stars can be approached in new ways. Machine learning methods can be employed to classify large numbers of evolved massive stars. These classifications can then be used to constrain stellar evolution models using novel statistical methods. In particular, I will show how data from next-generation observatories can be used to constrain the massive star binary fraction. Finally, I will highlight my recent work using TESS data to reveal never-before-seen high-frequency microvariability in yellow supergiants. I will discuss the two observed “flavors” of YSG microvariability, and touch on the importance of this discovery for massive star evolutionary theory, asteroseismology, and the red supergiant problem.
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