AstroData Hack Week

Joshua Bloom

University of California, Berkeley

(bio coming soon)

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 data-intensive 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 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.

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 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.

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.

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 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.