Jeffrey Regier is a postdoctoral research at UC Berkeley in the Department of Electrical Engineering and Computer Science. His research focuses on Bayesian modeling, variational inference, and optimization for large-scale scientific applications. Jeff holds a PhD in statistics from UC Berkeley, as well as MS degrees in mathematics (UC Berkeley) and computer science (Columbia University).
Astronomical surveys are the primary source of information about the Universe beyond our solar system. They are essential for addressing key open questions in astronomy and cosmology about topics such as the life-cycles of stars and galaxies, the nature of dark energy, and the origin and evolution of the Universe.
We are developing new methods for constructing catalogs of light sources, such as stars and galaxies, for astronomical imaging surveys. These catalogs are generated by identifying light sources in survey images and characterizing each according to physical parameters such as brightness, color, and morphology. Astronomical catalogs are the starting point for many scientific analyses, such as theoretical modeling of individual light sources, modeling groups of similar light sources, or modeling the spatial distribution of galaxies. Catalogs also inform the design and operation of follow-on surveys using more advanced or specialized instrumentation (e.g., spectrographs). For many downstream analyses, accurately quantifying the uncertainty of parameters' point estimates is as important as the accuracy of the point estimates themselves.
Our approach is based on Bayesian inference---a highly accurate method that is notorously demanding computationally. We use supercomputers containing the latest Intel hardware to quickly solve our Bayesian inference problems.
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