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 Astro Data Lab, where we develop new algorithms to change how astronomy is done.

My central research objective: how to optimally combine multiple data sets at the pixel level, in particular for the upcoming surveys Rubin, Euclid, and Roman. We develop techniques for source separation, mixture modeling, and fast inference, using generative modeling with and without neural networks.

On an even larger scale, we optimize the full scientific duty cycle, from observing strategy to data analysis, for precision measurements and discovery potential. Funded by the Schmidt Futures Foundation, we build new statistical and machine learning methods for target selection and physical modeling for the spectra of the upcoming PFS survey.

I also lead the method development of the NSF-funded Convergence Accelerator project HydroGEN, where we build a physics-based machine learning platform for hydrologic scenario generation to predict droughts, wildfire conditions, and the availability of drinking water for the entire continental United States.