My research interests lie in statistical methods for large surveys. I lead the Astronomy Data Group, where we develop algorithms that accurately capture the properties of billions of stars and galaxies despite the limitations of real-world instruments. I hold a joint position at the Department of Astrophysical Sciences and the Center for Statistics and Machine Learning.
My central research question right now is: how can we optimally combine multiple data sets to extract more information than from an individual analysis. My group designs the system that combines data from the upcoming surveys LSST, Euclid, and WFIRST at the pixel level. We develop techniques for source separation, mixture modeling, and data fusion, using proximal techniques and, increasingly, neural networks. I am also PI of a project funded by the Schmidt Futures Foundation to use modern statistical tools to optimize the target selection for the upcoming survey with PFS.