My research interests include Bayesian modeling and computation, astrostatistics, machine learning methods, and measuring statistical information especially in the context of experimental design. I have been a member of the international CHASC Astrostatistics collaboration since 2010.
Some of my current focuses are:
Statistical and machine learning methodology for exoplanet detection in the presence of stellar activity. This is essentially a signal detection problem where the data are a high dimensional time series and the noise is highly structured and non-stationary.
Bayesian hierarchical modeling and inference for images in astronomy and neuroscience. Goals include faint object detection, overlapping object separation, and characterization of transient phenomena.
Developing interpretable measures of statistical information for classification, model selection, and hypothesis testing problems. The information we need differs by context and I am interested in the information overlap and separation when considering several inference goals. I apply this to the problem of scheduling follow-up telescope observations in order to better classify astronomical lightcurves (time series).
Scalable Bayesian clustering and its application to astronomy.
Methodology and theory for constructing efficient Monte Carlo estimators of normalizing constants (or integrals more generally), especially in the context of multi-modal densities. I also apply this methodology to exoplanet detection.
Before joining Texas A&M, I was a postdoctoral fellow at Duke University and SAMSI where I was involved in the SAMSI ASTRO program. I received my Ph.D. in Statistics in 2016 from Harvard University under the guidance of Prof. Xiao-Li Meng.