I am a graduate of Harvard University with a Bachelor of Arts in Statistics (Data Science) and a minor in Government. Raised in Grand Rapids, Michigan, I gained experience as a political organizer and campaign manager, leveraging data to win important local elections during the competitive 2016, 2018, and 2020 election cycles. Now, I work as a data scientist, bringing my passion, technical know-how, and strong communication skills to new challenges at Booz Allen Hamilton.
About Me
Gibbs sampling is one of the most popular Markov Chain Monte Carlo (MCMC) algorithms with applications in Bayesian statistics, computational linguistics, political science, and more. In my first article published with Towards Data Science, I explained how Gibbs sampling really works through a series of visualizations.
Read the ArticleUse permutation feature importance to discover which features in your dataset are useful for prediction.
Read the Article
R Shiny app on 2020 Ivy League Men’s Basketball statistics with interactive visualizations for easy comparison.
Launch ApplicationOver 2+ years of experience working in the intersection of technology and government.
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