I am a Master's student in Statistical Science at Duke University, with interests in Bayesian modeling and programming, Bayesian nonparametrics, as well as machine learning methods. The intersection between statistics and healthcare is specifically of interest to me.
From August 2016 through May 2019, I worked at the Analysis Group in Boston as an analyst/senior analyst. Specifically, I worked on litigation economic matters in which I helped support academic experts who present empirical findings to a jury, as well as health economics and outcomes research (HEOR) and health strategy projects in which I helped build statistical models and develop academic manuscripts for bio-pharmaceutical companies.
I am passionate about building and drawing lessons from rigorous data-based models to better understand real-world data patterns. One of my favorite extracurriculars is applying the techniques I enjoy using in academic work to baseball. In 2015 and 2016, I worked for the Washington Nationals in the capacity of sabermetrics research, and in the fall semester of 2018, I was a visiting lecturer at Tufts University in Baseball Analytics.
I received my bachelor's degree in Quantitative Economics and Mathematics (minor of Computer Science) from Tufts University, where I had the opportunity to gain an interdisciplinary exposure to statistical modeling through the economics, math, and computer science departments.
Wysham, Carol H., et al. “Development of risk models for major adverse chronic renal outcomes among patients with type 2 diabetes mellitus using insurance claims: a retrospective observational study.” Current Medical Research and Opinion, Nov. 2019, pp. 1–9. Epmc, doi:10.1080/03007995.2019.1682981. Full Text
Young, James B., et al. “Development of predictive risk models for major adverse cardiovascular events among patients with type 2 diabetes mellitus using health insurance claims data.” Cardiovascular Diabetology, vol. 17, no. 1, Aug. 2018, p. 118. Epmc, doi:10.1186/s12933-018-0759-z. Full Text