Astro Hack Week is a week-long summer school / hack week / unconference
focused on astrostatistics and data-intensive astronomy.

The mornings will be a typical summer school format, with lectures and exercises covering essential skills for working effectively with large astronomical datasets. The afternoons will be entirely unstructured, and offer opportunities for collaborative research, breakout sessions on special topics, and application of the concepts covered during the morning sessions. The vision is to provide a space to encourage learning, research, collaboration, and sharing of expertise, for the benefit of both young and experienced astronomical researchers alike.

Meet the organisers of Astro Hack Week 2015.

Daniela Huppenkothen

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

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, Gaia, NYU-VAGC, and SDSS-III.

Phil Marshall

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 van der Plas

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

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

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.

Live Stream

Follow the live stream below. You can tweet us questions with hashtag #AskAHW or general comments with hashtag #AstroHackWeek. Be aware that there is a 30-60 second delay between the recording and the live stream.


This workshop is being organized as part of the Moore-Sloan Data Science Initiative, together with New York University, the University of Washington and University of California Berkeley. It is made possible by the following sponsors:

Ready to hack?

You can now sign up for Astro Hack Week! Registration is open until June 15.
Just click on the link below and answer a few questions.