Danny Goldstein is a second-year graduate student in the astronomy department at UC Berkeley. He graduated summa cum laude from the University of Pennsylvania, where he received a Bachelor of Arts with distinction in Physics, and was inducted into Phi Beta Kappa. He is interested in the application of machine learning techniques to a wide variety of problems in observational and theoretical astrophysics. Danny is a member of the Dark Energy Survey's supernova working group, where he led the development of a framework to automate the discovery of astrophysical transients using Random Forest. At C3, he is collaborating with Rollin Thomas on a novel application of hierarchical Bayesian surrogate models to Type Ia supernova simulations. Their machinery will use Gaussian Process regression to instantaneously predict the results of expensive, massively parallel SN simulations. When he's not coding at C3, Danny likes catching movies at the Castro Theatre in San Francisco, playing Badminton, and listening to KALX Berkeley, 90.7 FM.