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 new algorithms to change how astronomy is done.

My central research objective: how to optimally combine multiple data sets. My lab designs a system that combines data from the upcoming surveys from Rubin, Euclid, and Roman 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 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.