Education

Ph.D. in Statistics, University of Florida

  • Dissertation: Bayesian Methods for Estimating Optimal Biomarker Composites to Quantify Progression in Duchenne Muscular Dystrophy
  • Advisor: Michael J. Daniels

B.S. in Statistics, B.A. in Mathematics, University of North Carolina at Chapel Hill

Graduate Research

My doctoral research focuseed on Bayesian modeling for incomplete longitudinal data in order to quantify disease progression of individuals with Duchenne muscular dystrophy (DMD). This work involved Markov Chain Monte Carlo (MCMC) algorithms, structured correlation models, and joint nonlinear mixed-effects (NLME) models.

Undergraduate Research

My undergraduate research focused on analyzing the citation network of the Supreme Court of the United States (SCOTUS), as well as using natural language processing (NLP) to study SCOTUS text data.