AAS 230 Special Session: Topics in Astrostatistics

Vinay Kashyap and Aneta Siemiginowska (of the CHASC AstroStatistics Collaboration and the
Harvard-Smithsonian Center for Astrophysics) have arranged a Special Session entitled “Topics in Astrostatistics” to explore the intersection of observational astronomy, statistics, and data science. Large, complex, high-quality datasets from modern instruments pose unprecedented challenges. Functional literacy in astrostatistics is becoming a prerequisite for the careful analysis of such data and the proper accounting for uncertainties in it. This, in turn, requires descriptive, science-driven statistical models and methods that relate underlying physical processes to observables.

These issues will be considered by a stellar lineup of experts beginning on the morning of Monday, 5 June. First up is Yang Chen (University of Michigan), who will present “The Bayesian Statistics Behind Calibration Concordance.” She’ll be followed by Chad Schafer (Carnegie Mellon University) on “The Potential of Deep Learning with Astronomical Data,” and Pavlos Protopapas (Harvard Institute for Applied Computational Science) on “Recurrent Neural Network Applications for Astronomical Time Series.”