I am actively working in several applied and theoretical areas.
I have worked extensively on developing both methods and theory
for solving various problems in
astronomy and cosmology. This research introduced me to the field known as
inverse problems, where the observed data are actually noisy version
of smooth functionals of the
object of interest.
Additionally, I have spent a fair amount of time investigating the prediction risk
implications of empirical tuning parameter selection for lasso-type methods
More recently, I have become interested in addressing some of the philosophies
that currently dominate the field of macroeconomic forecasting. Most notably,
the overparameterization and complexity that results the over reliance on microeconomic foundations
for doing predictions. Lastly, I work on examining the statistical implications of computational
approximations. More specifically, I have a grant for examining the intriguing possibility
that these approximations can actually improve statistical performance.
For publications, click
I have a bachelors degree from the University of Colorado in
economics and math, and a masters and Ph.D. from Carnegie Mellon
University in statistics, under the direction
of Chris Genovese. Outside of academia, I enjoy
hiking, running, weight training, playing and watching sports, and,
perhaps most of all, coffee.
If you would like to work with me, please send me an email with a brief description of your research interests.
We'll set up a meeting to chat.