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 (spatial, temporal, functional, etc.) for a variety of applications including official statistics, social sciences, and environmental sciences. 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
Find a PDF of my CV here.
In a world driven by data, few fields are as impactful and essential as official statistics. Whether it’s tracking poverty rates, monitoring public health trends, or guiding economic policies, the field of official statistics serves as the backbone of informed decision-making in society.
[Note: This post was created as part of a lecture for STAT 131 at UCSC.] Recall that for two continuous random variables \(X\) and \(Y\), we work with the joint probability density function \(f(x,y)\).
If you work with any 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.