We talk a lot about process—not outcome—and trying to consistently take all the best information you can and consistently make good decisions. Sometimes they work and sometimes they don’t, but you reevaluate them all.
I am a recent graduate of the Master’s of Statistical Practice program at Carnegie Mellon University. My interests lie in the intersection of computing and statistical analyses. More specifically I strive to use modern computing techniques and frameworks in conjunction with classical statistical methods to provide meaningful insights from data. For an overview of my work be sure to check out my resume!
My past research centered on the effectiveness of mask mandates in spurring mask compliance and prevelance of Covid-19 in communities. I worked with Dr. Alex Reinhart using data from Carngie Mellon’s Delphi Group and the University of Washington’s Covid-19 State Policy Group to quantify the marginal effect of county and state-level mask mandates. Earlier research focused on the development of a modern statistical learning tool in conjunction with Dr. Philipp Burckhardt under the supervision of Dr. Rebecca Nugent. The tool, ISLE, tracks student progress while making data science interactive at all stages of the development lifecycle.
I received my BS in Statistics and Machine Learning from Carnegie Mellon University in 2019. During my time as an undergrad, I was heavily involved in the development and implementation of 36-200, the first-semester introductory statistics course using ISLE. Additionally I was active in residence life as a resident assistant for two years and a head RA during my senior year managing a team of 10 RAs and 300 first year residents.
In my spare time I have written R packages for personal and professional use. I enjoy sports of all sorts, trashy reality TV and strategy games.
For more specific information about me feel free to explore the pages listed above or contact me!