I'm Assistant Professor of Statistical Astronomy at Princeton University's Department of Astrophysical Sciences and the Center for Statistics and Machine Learning. I lead the Princeton Astronomy Data Lab, where we develop algorithms that accurately capture the properties of billions of stars and galaxies despite the limitations of real-world instruments.
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 lab designs a 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 neural networks.
On an even larger scale, we optimize the full scientific duty cycle, from instrument design to observing strategy to data analysis for maximum yield: precision measurements and discovery potential. Funded by the Schmidt Futures Foundation, we build modern statistical and machine learning methods, focussing on the target selection of the upcoming PFS survey.