About
I’m an Associate Teaching Professor at Emory University in the Department of Data and Decision Sciences, where I teach Bayesian statistics and machine learning and direct undergraduate research.
My training is in probabilistic methods: a PhD in political methodology and an MA in Statistics from the University of Michigan, where the work was Bayesian inference and computational methods applied to messy human data — elections, institutions, behavior. Before that path, I spent two years at Amazon building Weblab, the A/B experimentation platform, which left me with a patent on long-term effects in hypothesis testing and a lasting conviction that measurement design is where analyses are won or lost.
The through-line in everything since — the courses, the book in progress, the applied programs, the ML & Sports Analytics Lab I founded — is the same: modern machine learning is applied probability, human knowledge enters models through priors at every level, and both building and teaching these systems well means being honest about that.
Away from the university: I build and perpetually upgrade computers — mostly hunting killer deals on parts I don’t strictly need; nobody needs this many cases — chase roller coaster credits across the country (Intimidator 305 is the only one that’s ever grayed me out), hike, climb, and hold lifelong barbecue opinions inherited from my dad. Lately I’m deep in the Metroidbrainia genre: Tunic is a masterpiece, and if you haven’t played it, go in blind.
Awards and recognition
- U.S. Patent 10,152,458 B1 — a method for determining long-term effects in statistical hypothesis testing, out of the Weblab work at Amazon.
- John T. Williams Dissertation Prize — Society for Political Methodology.
- Samuel Eldersveld Outstanding Paper Award — University of Michigan.
- Most Influential Professor — Emory University, 2024 and 2025.
The full record is in the résumé.