Astro Hack Week is a weeklong summer school / hack week / unconference focused on astrostatistics and dataintensive 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.
Hack Week Wiki
See our github wiki for uptodate information on scheduling and planning, including a partial list of who will be attending and what they plan to work on!
Registration
This will be a relatively small event; we have space for about 40 people. Registration is $50 per participant, which will partially offset the costs of running the workshop.
We have reserved a block of rooms at the University Inn, a short walk from campus; the conference rate of $149 (single or double occupancy) is available firstcome firstserved.
People
The following folks have been involved with planning this event, and will be teaching the morning sessions and facilitating the afternoon hack times:
Joshua Bloom
Joshua Bloom
University of California, Berkeley
(bio coming soon)
Željko Ivezić
Željko Ivezić
Željko Ivezić is a professor of astronomy at the University of Washington. His research interests are in detection, analysis and interpretation of electromagnetic radiation from astronomical sources. He is lead author of Statistics, Data Mining, and Machine Learning in Astronomy, a graduate textbook for dataintensive astronomy. He serves as the Project Scientist for the Large Synoptic Survey Telescope project and currently chairs the American Astronomical Society's Working Group on Astroinformatics and Astrostatistics.
David W. Hogg
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 longterm goals, but he is involved in presentday projects that work towards them, including Astrometry.net, Gaia, NYUVAGC, and SDSSIII.
Daniela Huppenkothen
Daniela Huppenkothen
Daniela Huppenkothen is a graduate student close to completing her PhD at the Anton Pannekoek Institute for Astronomy at the University of Amsterdam, and will join the NYU Center for Data Science as a postdoctoral fellow in late autumn. 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 Xray 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.
Phil Marshall
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 groundbased 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.
Fernando Perez
Fernando Perez
Fernando Perez received his PhD in theoretical physics from the University of Colorado and then worked on numerical algorithm development at the Applied Mathematics Dept. at the same university. In 2001, he became involved with the nascent scientific Python community, and hasn't left. He created IPython while a graduate student looking for efficient interactive research tools. He continues to lead the IPython project along with a talented team that does all the hard work. He is currently a research scientist at the Helen Wills Neuroscience Institute at the University of California, Berkeley, focusing on the development of new tools for scientific computing and data science, drawing from problems in brain imaging and other disciplines.
Perez regularly lectures about scientific computing in Python, and is a member of the Python Software Foundation as well as a founding board member of the Numfocus Foundation. He is the recipient of the 2012 Award for the Advancement of Free Software from the Free Software Foundation.
Jake VanderPlas
Jake VanderPlas
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 coauthor of the graduatelevel 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, Scikitlearn, mpld3, astroML, and others. He occasionally blogs about Python, machine learning, scientific visualization, open science, and related topics at Pythonic Perambulations.
Schedule
Each day will consist of morning sessions devoted to learning
fundamentals of computational astronomy, and afternoon sessions
devoted to unstructured research, hacking, and collaboration,
with the potential
for informal breakout sessions on special topics of interest.
We will be making use of the textbook Statistics, Data Mining, and Machine Learning in Astronomy by Ivezić, Connolly, VanderPlas, and Gray. It is not required, but we encourage you to bring a copy if you have it!
The following is an initial draft of the schedule; there will likely be
modifications made in the weeks leading up to the conference.
Monday, September 15th
9:00am9:30am 
Coffee 
9:30am12:00pm 
Scientific Computing with Python
 Interactive Computing & Reproducible Research with IPython
 Effective Computing with NumPy
 Visualization with Matplotlib
 Exploring computational tools available in SciPy

12:00pm12:30pm 
Hack Idea Intros 
12:30pm1:30pm 
Lunch onsite 
1:30pm5:30pm 
Hack time & Breakouts 
5:30pm6:00pm 
Daily Wrapup 
Tuesday, September 16th
9:00am9:30am 
Coffee 
9:30am12:30pm 
Classical Statistics & Modeling
 Intro to classical probability theory
 Maximum likelihood Optimization & Uncertainty Quantification
 Goodness of Fit and Hypothesis Testing
 Confidence Estimates using Bootstrap

12:30pm1:30pm 
Lunch onsite 
1:30pm5:30pm 
Hack time & Breakouts 
5:30pm6:00pm 
Daily Wrapup 
Wednesday, September 17th
9:00am9:30am 
Coffee 
9:30am12:30pm 
Bayesian Statistics & Modeling
 Bayes' Theorem and Bayesian probability
 Bayesian Priors
 Posterior optimization, marginalization, and Uncertainty Quantification
 Hypothesis Testing
 Intro to Markov Chain Monte Carlo (MCMC) sampling

12:30pm1:30pm 
Lunch onsite 
1:30pm5:30pm 
Hack time & Breakouts 
5:30pm6:00pm 
Daily Wrapup 
Thursday, September 18th
9:00am9:30am 
Coffee 
9:30am12:30pm 
Supervised Machine Learning & Model Fitting
 Supervised Machine Learning: Classification vs Regression
 A survey of Classification techniques
 A survey of Regression techniques

12:30pm1:30pm 
Lunch onsite 
1:30pm5:30pm 
Hack time & Breakouts 
5:30pm6:00pm 
Daily Wrapup 
7:00pmlate 
offsite dinner and hackathon 
Friday, September 19th
9:00am9:30am 
Coffee 
9:30am12:30pm 
Unsupervised Machine Learning & Data Mining
 Dimensionality Reduction Algorithms
 Clustering Algorithms
 Density Estimation Algorithms

12:30pm1:30pm 
Lunch onsite 
1:30pm5:00pm 
Hack time & Breakouts 
5:00pm6:00pm 
Week Wrapup 
Because of the unique and unstructured nature of this hack week, we
suggest that you come prepared! Come with a project in mind, with data
to explore, with a colleague to collaborate with. This should not be a
week away from work, but a week to try some new approaches to your
current research topic!
Sponsors
This workshop is being organized as part of the MooreSloan Data Science Initiative, together with the University of Washington, University of California Berkeley, and New York University. It is made possible by the following sponsors: