Daniela Huppenkothen is a Moore-Sloan Postdoctoral Fellow at the NYU Center for Data Science. She is primarily interested in time series methods for astronomy; so far, her work has focussed on developing methods for characterising variability in fast transient events (in particular magnetar bursts) in data from X-ray space telescopes, and on using empirical models to make inferences about the underlying physics of the system. She is also interested in machine learning and astrostatistics.

David W. Hogg is a professor of Physics and Astronomy at New York University. His main research interests are in observational cosmology, especially approaches that use galaxies (including our own Milky Way) to infer the physical properties of the Universe. He also works on exoplanet measurement and discovery. In both areas, he is interested in developing the engineering systems that make these projects possible, for his group and for the astrophysics community as a whole. His research is supported by New York University, NASA, the NSF, and the Humboldt Foundation. Hogg is working towards several comprehensive projects in observational astrophysics, including the measurement and simultaneous analysis of every galaxy (above some mass) in the observable Universe, every star (above some brightness) in our Galaxy, or every image (above some quality) taken by any astronomical camera. The comprehensive goals are long-term goals, but he is involved in present-day projects that work towards them, including Astrometry.net, Gaia, NYU-VAGC, and SDSS-III.

Phil Marshall is a staff scientist at the Kavli Institute for Particle Astrophysics and Cosmology, at SLAC, Stanford University. His main research interest is observational cosmology using gravitational lensing: weighing galaxies, and measuring the expansion rate of the Universe. He is involved in a number of surveys to find new lenses, using both ground-based and space telescopes - including designing the strong lensing science analysis for LSST. Like all astrophysicists Marshall works in the low signal to noise regime, where information is at a premium and prior knowledge inevitably becomes important at some stage: developing probabilistic methods for data analysis is a continuing theme in his work.

Jake VanderPlas is the Director of Research in the Physical Sciences at the University of Washington’s eScience Institute, where his research is primarily in the area of novel statistical approaches to datasets from astronomy and other fields. He is co-author of the graduate-level text, Statistics, Data Mining, and Machine Learning in Astronomy, and is a maintainer and/or frequent contributor to many scientific open source Python projects, including SciPy, Scikit-learn, mpld3, astroML, and others. He occasionally blogs about Python, machine learning, scientific visualization, open science, and related topics at Pythonic Perambulations.

Laura Norén is a postdoctoral associate doing ethnographic research at New York University's Center for Data Science. Her research focuses on organizational sociology, ethnography, and computational sociology.

Kyle Barbary is a Cosmology Data Science Fellow at UC-Berkeley and the Berkeley Institute for Data Science. He studies cosmology using Type Ia supernovae as part of the Nearby Supernova Factory (SNfactory) and the Dark Energy Survey (DES). He is currently interested in robust statistical inference from (photometric-only) supernova datasets and the development of sustainable software for that purpose. He contributes to a number of open-source astronomy packages in Python and Julia.