Across almost all scientific disciplines, the instruments that record our experimental data and the methods required for storage and data analysis are becoming increasingly sophisticated. This has been particularly true for astronomy, where current and future instruments produce data sets of a size and complexity that are impossible to make sense of with traditional methods. In this talk, I will focus on recent research in time domain astronomy and present examples of how we can use modern statistical and machine learning methods to help us explore and understand the physical processes underlying a diverse range of phenomena, from the composition and shape of asteroids to accretion physics in black holes. I will also show how in the future, these new methods and tools will aid us in making sense of data sets from large surveys like LSST.
The universal applicability of data science tools to a broad range of problems has also generated new opportunities to foster an exchange of ideas and computational workflows across disciplines. I will discuss ways to enable interdisciplinary collaboration in order to solve fundamental problems across multiple domains.