Python in Astronomy brings together researchers, Python developers, users, and educators. The conference will include presentations, tutorials, unconference sessions, and coding sprints. In addition to sharing information about state-of-the art Python Astronomy packages, the workshop will focus on improving interoperability between astronomical Python packages, providing training for new open-source contributors, and developing educational materials for Python in Astronomy. The meeting is therefore not only aimed at current developers, but also users and educators who are interested in being involved in these efforts.
I am a Cosmology Data Science Fellow at the Berkeley Center for Cosmological Physics. As a cosmologist, I study the "Universe at large": how the Universe has expanded over time and the properties of dark energy, its largest component. I do this using Type Ia supernovae, a type of stellar explosion that can be used as an indicator of distance. These distance indicators allow us to measure how the universe has expanded over the past 10 billion years, looking back to the first third of the Universe's existence!
I'm a member of two supernova experiments: the Nearby Supernova Factory (SNfactory) and the Dark Energy Survey (DES). SNfactory is an experiment on a telescope in Hawaii and is designed to study nearby Type Ia supernovae in great detail in order to enhance their use in cosmology. DES is a five-year imaging program on a telescope in Chile designed to discover distant supernovae and to measure dark energy with unprecedented precision.
My recent research focus has been on writing reusable open-source scientific software for astrophysics research. I'm a core contributor to the AstroPy project, a community-developed astronomy library for Python. I also develop a Python package specifically for supernova cosmology research called SNCosmo. I've also started developing packages for the Julia programming language, including both astronomy packages and general purpose scientific packages. I’m particularly interested in making it as easy as possible for scientists to find, use, and understand the software they need to accomplish their research.