# Daniela Huppenkothen

I’m interested in using time series to understand the physics of black holes and neutron stars, especially using astrostatistics, a field that makes modern statistical methods useful for astronomical data analysis. I have worked on a number of problems, including methods to help us understand variability in fast transients like magnetar bursts, and using machine learning to classify time series from black hole X-ray binaries. At DIRAC, I am hoping to combine X-ray and optical data to make our inferences about black holes better, among other things. I am also interested in how we can use machine learning and statistics to mitigate biases introduced into our data by detectors. I am lead developer for a software project called Stingray, which aims to build a standard implementation of a lot of time series methods used in astronomy. Beyond astronomy, I am interested in finding new ways to teach data science to astronomers as a co-organizer of AstroHackWeek, and I think about participant selection for scientific workshops a lot (via the software project Entrofy).