Across almost all scientific disciplines, the instruments that record our experimental data and the methods required for storage and data analysis are rapidly increasing in complexity. This has been particularly true for astronomy, where current and future instruments produce data sets of a size and complexity not accessible with traditional methods. In this talk, I will focus on recent research in astronomy and present examples of how we can use modern computational tools to help us understand the accretion and radiation processes around stellar-mass black holes. I will show recent results using multi-year observations from X-ray space telescopes that draw upon machine learning in order to help us disentangle the different physical states in these systems.
The universal applicability of data science tools to a broad range of problems has also generated new opportunities to foster 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.