About
I’m a biostatistician and data scientist finishing my PhD at the Johns Hopkins Bloomberg School of Public Health, advised by Brian Caffo and Stephanie Hicks, where I’m a Vivien Thomas Scholar and a Center of Excellence in Regulatory Science and Innovation (CERSI) Scholar. At the Ludwig Maximilian University of Munich’s Institute for Statistics, I’m a visiting researcher working with Professor Sabine Hoffmann, first under a Fulbright research grant I proposed and held as principal investigator and now through the Vivien Thomas Scholars Initiative at Johns Hopkins.
My dissertation, The Well-Made Analysis, develops methods that make data analysis defensible by design: criteria for knowing when the work is finished, a measure of how much evidence a public dataset has left after many studies of it, and safeguards for the artificial intelligence (AI) agents that have started running analyses themselves. I expect to defend it in March 2027. I also apply these methods in global-scale epidemiology, where studies I’ve co-authored have been cited in more than 200 policy and guideline documents. Before my doctorate, I worked in information security and data governance in industry, and I was selected as a National Aeronautics and Space Administration (NASA) Datanaut. My technical writing and open-source guides have been featured by Google, Microsoft, Forbes, the American Statistical Association, and the Association for Computing Machinery.
I hold a Bachelor of Science in Mathematics from the University of California, Irvine and a Master of Science in Applied and Computational Mathematics from Johns Hopkins University. My work has been cited more than 30,000 times, and the R packages I’ve authored and maintained have more than 50 million downloads.