Professional Impact

I'm a Bayesian statistician with 20+ years of experience in academics and industry. Much of my work has been as a data science enabler, by building tools that data scientists can use in their analyses. I'm an expert in Bayesian computation, specializing in applied problems using Markov chain Monte Carlo. I have extensive experience with hidden Markov models, time series modeling, mixture models, hierarchical models, and various forms of Bayesian regression.

I have held director-level positions at Google and Capital One, and served on the faculty at USC's Marshall School of Business.

Multi-armed bandit experiments

Multi-armed bandit experiments are a way of conducting A/B tests (and more general sequential experiments) with much lower opportunity cost than a traditional statistical experiment.

My work on multi-armed bandits became the basis for Content Experiments by Google Analytics, and found its way into parts of the ad system as well.

For an overview of the Thompson sampling method for multi-armed bandits, see the papers and slides below.

• A modern Bayesian look at the multi-armed bandit.
• Multi-armed bandit experiments in the online service economy.