Astronomers deal with huge datasets, and they are about to get even bigger with the construction of the Vera Rubin Observatory. When you can detect a million supernovae per year, how do we make sense of this data and decide which ones are the "most interesting" to study? Professor Ashley Villar at the Center for Astrophysics | Harvard & Smithsonian has made her career out of developing machine learning techniques to answer this very question.