I am currently an assistant professor in the Department of Statistics at the University of California, Santa Cruz. Before this role, I obtained my Ph.D. in Statistics at the University of Missouri, where I was a recipient of the U.S. Census Bureau Dissertation Fellowship, and a recipient of the University of Missouri Population, Education and Health Center Interdisciplinary Doctoral Fellowship. My dissertation work was focused on Bayesian methods for modeling non-Gaussian unit-level survey data under informative sampling, with an emphasis on application to small area estimation. I am broadly interested in modeling dependent data (time-series, spatial, functional, etc.) for a variety of applications including official statistics, social sciences, and ecology. I am also interested in integration of modern machine learning and data science techniques to help improve statistical models.
Ph.D. in Statistics, 2021
University of Missouri
M.A. in Statistics, 2018
University of Missouri
B.S. in Applied Mathematics, 2014
University of Idaho
If you work with Census Bureau data, you are probably familiar with Public Use Microdata Samples (PUMS) from the American Community Survey. These are individual records (either person or household) that allow data users more customized analysis than would be available with the ACS tabulated data products.
I have recently been intrested in modeling stock volatility. In order to make the process easier, I wanted a way to quickly download stock data in R, and after some quick searching on the web, I stumbled across the tidyquant package which is able to download stock data from Yahoo Finance, and then conveniently store the data as a tibble object.
Find a PDF of my CV here.