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 solve problems that hold back astronomy.

We seek to optimally extract information about a process from messy observations of that process. Messy can mean noisy, incomplete, censored, unstable, glitchy, heavily contaminated, and often all of the above. How can we deal with that? By assuming that physics works, even though any theorical model is incomplete. My group therefore creates novel statistical techniques by combining physical principles with deep learning. We develop methods for signal separation, data fusion, fast inference, and outlier detection for large astronomical surveys.

We also optimize the full scientific duty cycle, from observing strategy to data analysis, for precision measurements and discovery potential. Funded by Schmidt Sciences, we optimize the target selection and utilization of spectra for the upcoming PFS survey.

My research on physics-ML hybrids applies to remote sensing, environmental sensing, and experimental devices. For the NSF-funded HydroGEN project, I lead the method development of 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.

For Students
If you're already at Princeton: I usually have projects for undergraduate or graduate students, so come talk to me. As PhD applicant, please directly apply to the department. After you have been accepted, I'm happy to talk about projects.