I am a computational statistician. I specialize in areas of real-data analysis in the physical, engineering and biological sciences where the ideal modeling apparatus is impractical, or where the current solutions are inefficient and thus skimp on fidelity. Such endeavors often require new models, new methods, and new algorithms. My goal is to be impactful in all three areas while remaining grounded in the needs of a motivating application. I aim to release general purpose software for consumption by the scientific community at large, not only other statisticians.
One example comes from my Ph.D. work, where I helped
NASA design a computer experiment for a re-usable rocket
booster. The software developed for this project,
R, has since
found wide applicability in areas as diverse as insurance,
economics, climate science, epidemiology, and finance. My
dissertation won three awards
including the Savage
Award for best thesis in applied Bayesian methodology. In 2017 I received a Facebook faculty award for my research on large scale surrogate modeling and Bayesian optimization.
Fast-forward to the present: I am excited about a new
R package called
which is aimed at big data regression, and computer model emulation,
by local approximate Gaussian processes.
The code in the package, which facilitates
has been used to tackle a
large-scale computer model calibration problem arising in a
radiative shock hydrodynamics,
and for blackbox constrained optimization
in a benchmark groundwater remediation exercise. I am currently updating the methods for a
cool application on predicting the atmospheric drag of satellites in orbit. This is important for
positioning and collision detection. The University of Chicago
Research Computing Center (RCC),
to whom I am grateful for valuable high-performance computing (HPC) resources, did a puff piece on this work-in-progress.
My research is currently funded by the following awards.
- NSF CDS&E-MMS 1521702: for optimal stochastic control, heteroskedastic Gaussian process regression, and design of computer experiments with applications to epidemiology and UAV tracking.
- NSF CDS&E-MMS 1621746:
extending local approximate Gaussian processes (
laGP) as motivated by large scale response surface modeling of satellite drag and solar irradiance.
- SciDAC; DOE Office of Science ASCR and High Energy Physics: new methods and algorithms in the area of extreme-scale inference and machine learning motivated by surrogate modeling an inverse problem challenges in cosmology.
The folks listed below are currently, or were until recently, members of my lab.
I am a member of ISBA, ASA, IMS, INFORMS, SIAM, currently serve as treasurer for the International Society for Bayesian Analysis (ISBA), program chair for the ASA section on Statistics in Defense and National Security (SDNS) , and program chair elect for the ASA Section on Bayesian Statistical Science (SBSS).
I currently serve on the editorial board for Technometrics, Journal of Uncertainty Quantification, and Bayesian Analysis, and recently as guest editor for a Statistica Sinica special issue on Uncertainty Quantification. Each year I referee about ten papers for other journals.