I am a Principal Statistical Consultant at Novartis Pharmaceuticals. I work in the Advanced Exploratory Analytics (AEA) group of the Advanced Methodology & Data Science (AMDS) team.
My research focuses on developing methods with finite sample validity in flexible settings and applying modern statistical methods to biomedical problems.
I completed my PhD in Statistics at Carnegie Mellon University in July 2021. I was fortunate to work with Professors Larry Wasserman (co-advisor), Aaditya Ramdas (co-advisor), and Sivaraman Balakrishnan on universal likelihood ratio testing. The universal LRT provides tests with valid type I error control in any setting where we can write a likelihood ratio (or upper bound the null maximum likelihood). My thesis is available here.
PhD in Statistics, 2021
Carnegie Mellon University
MS in Statistics, 2017
Carnegie Mellon University
BA in Mathematics, 2016
Kenyon College
Summer 2019: 36-315: Statistical Graphics and Visualization. Syllabus.
Developed course materials, presented lectures, led labs, and held office hours.
Topics: choosing and interpreting graphics, mastering ggplot, interactive graphics with Shiny.
Summer 2018: Summer Undergraduate Research Experience graduate advisor.
Mentored three undergraduate students to identify research directions on data science for justice. Provided guidance on R tools (ggplot, data.table, shapefiles) and statistical models (generalized linear models, random forests, spatiotemporal ETAS models). Students presented their final work at a departmental seminar and at the poster session of the American Statistical Associationâ€™s 2018 StatFest.
Carnegie Mellon University
Kenyon College