About The Team

Founders

Eric Novik is the CEO of Stan Group Inc. Prior to Stan Group, Eric was a Data Scientist at TIBCO Spotfire where he built statistical applications for customers in Financial Services, Energy, Pharma, and Consumer Goods Sectors. In 2010, Eric started Risktail, an options analytics software company for retail traders. During the financial crisis of 2007-2009, he traded listed options and later spent two years in the Equity Derivatives Technology group at Barclays Capital. He has an MA in Statistics from Columbia University.

Daniel Lee is an applied Bayesian statistician. He's worked with Andrew Gelman as a statistics researcher starting in 2009. He has a MASt (Master of Advanced Study aka Part III) from Cambridge and a B.S. in Mathematics with Computer Science (18C) from MIT. As a key member of the Stan development team, he is responsible for the architecture, design, and implementation of most of the C++ libraries and how they interact with each of the interfaces.

Ben Goodrich is a Stan developer and a Lecturer at Columbia University where he teaches in the Political Science department and the Quantitative Methods in the Social Sciences Master’s program. He is the maintainer of the rstan and rstanarm R packages, which provide interfaces to Stan. He has a Ph.D in Government and Social Policy from Harvard.

Jonah Gabry is a member of the Stan core development team and a statistician at Columbia University working with Andrew Gelman. He is also affiliated with the Columbia Applied Statistics Center, the Columbia Population Research Center, and the Institute for Social Economic Research and Policy. Jonah is co-author of the rstan and rstanarm R packages, which provide interfaces to Stan, as well as author and maintainer of the shinystan and bayesplot packages for model visualization, and the loo R package for model comparison.

Scientific Advisory

Andrew Gelman is a Professor of Statistics and Political Science and Director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin), Teaching Statistics: A Bag of Tricks (with Deb Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), and A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina).

Bob Carpenter is a Research Scientist in Computational Statistics at Columbia University. He designed the Stan probabilistic programming language and is one of the Stan core developers. Bob has a Ph.D. in Cognitive and Computer Science (University of Edinburgh), worked as a Professor of Computational Linguistics (Carnegie Mellon University), and an Industrial Researcher and Programmer in speech recognition and natural language processing (Bell Labs, SpeechWorks, LingPipe). In addition to working on Stan, he’s written two books on computational linguistics, many papers, and the widely used LingPipe natural language processing toolkit.

Michael Betancourt is a Postdoctoral Research Associate in the Department of Statistics at the University of Warwick where he develops theoretical and methodological tools to support practical Bayesian inference. He is also a core developer of Stan where he implements and tests these tools. In addition to hosting tutorials and workshops on Bayesian inference with Stan he also collaborates on analyses in epidemiology, pharmacology, and physics, amongst others. Before moving into statistics Michael earned a B.S. from the California Institute of Technology and a Ph.D. from the Massachusetts Institute of Technology, both in physics.