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.
I recently recieved NSF funding for a project blending optimal stochastic control, response surface modeling, and design of computer experiments. In early 2016 I recruited Mickaël Binois to join me as a postdoc, funded on the grant. We will work together to develop methods and computational tools for large scale heteroskedastic Gaussian process regression, as required by challenging dynamic control applications applications from finance, epidemiology, gas storage, and UAV tracking. Stay tuned.
I am a member of the following professional organizations: ISBA, RSS, ASA, IMS, CS, INFORMS, SIAM. I am the treasurer for the International Society for Bayesian Analysis (ISBA), and in 2017 I am the Program Chair-Elect for the Statistics in Defense and National Security Section of the ASA.
I currently serve on the editorial board for Technometrics and Bayesian Analysis. I recently served as guest editor for a Statistica Sinica special issue on Uncertainty Quantification. Each year I tend to referee about ten papers for other stats and domain-specific journals.