Teaching

Courses at UBC

📙 (2025/6) STAT 548 PhD Qualifying Course [description, paper list]

📙 (Winter 2026) STAT 547 Topics Class in Statistics: Bayesian Workflow [draft course website]

Books

I’m contributor to the following upcoming books.

📚 (2024+) Handbook of Markov chain Monte Carlo. Edited by Radu V. Craiu, Dootika Vats, Galin L. Jones, Steve Brooks, Andrew Gelman and Xiao-Li Meng. My chapter with Andrew Gelman: For how many iterations should we run Markov chain Monte Carlo?

📚 (2020+) Bayesian Workflow. Led by Andrew Gelman, Aki Vehtari, and Richard McElreath.

Outreach

Occasionally I write posts on the blog Statistical modeling, causal inference, and social science, primarily curated by Andrew Gelman. A list of my posts can be found here.

Other outreach efforts:

🎙️ (2024) I was co-guest with Andrew Gelman on the MetrumRG podcast on pharmacometrics. Our episode: Bayesian Workflow.

🎙️ (2024) I returned as a co-guest with fellow Stan developer Brian Ward on the podcast learning Bayesian Statistics, hosted by Alex Andorra. This was the first live episode on the podcast with an audience and it took place at StanCon 2024 at Oxford University.Our episode:Exploring the Future of Stan.

🎙️ (2024) I was a guest on the podcast learning Bayesian statistics, hosted by Alex Andorra. Our episode: Demystifying MCMC and Variational Inference.

📰 (2023) I was interviewed by science writer Mara Johnson-Groh for the Simons Foundation. The article Between knowing nothing and knowing for sure: the science of uncertainty provides an accessible overview of my research.

🎙️ (2021) I was a guest for the series Bayes. Uncertainty. Explained, hosted by Generable. Our episode: Some Outstanding Challenges when Solving ODEs in a Bayesian context.

Workshops and tutorials

I have now several times taught 1 – 3 days workshops on modeling and data analysis. My courses cover fundamental concepts in Bayesian modeling and some of the motivating theory, demonstrations on scientific examples, discussions of the “under-the-hood” algorithms that support statistical software, and hands-on coding exercises. Here’s a testimony on a workshop I gave in 2020 at McGill University. I quite enjoy teaching workshops on Stan and Torsten. If you’re interested in such a course, feel free to contact me!

I’ve been invited as an instructor for the following courses and conferences:

📙(2024) Monte Carlo Methods, Nordic Summer School on Probabilistic AI, Copenhagen, Denmark. (1/2 day workshop)[course material)]

📙(2023) Bayesian Workflow for hierarchical and ODE-based models, Summer School on Advanced Bayesian Methods, Leuven, Belgium. (3 day workshop) [course material]

📙(2023) Fundamentals of Stan. StanCon 2023. Washington University, St Louis, MO. (1/2 day workshop) [course material]

📙(2018 – 2022) Introduction to Probability and Bayes, PHC 506: Biometry in Pharmaceutics, University of Buffalo, School of Pharmacy, Buffalo, NY. [notes, erratum]

📙(2019 – 2022) Building, fitting, and criticizing Bayesian Pharmacokinetic/Pharmacodynamic models using Stan and Torsten, University of Buffalo, School of Pharmacy, Buffalo, NY. (1 day workshop) [outline]

📙(2019, 2020) Stan for the people: introductory workshop on Bayesian modeling, McGill University, Montreal, Canada. (2 days workshop)

📙(2019) Population and ODE-based models using Stan and Torsten, co-instructor with Yi Zhang, Stan Conference 2019, Cambridge, UK

📙(2018) How to Develop for the Stan C++ Core Language, Stan Conference 2018, Pacific Grove, CA. [slides]

📙(2017) Introduction to Bayesian Data Analysis with Stan, STAT 220: Bayesian Statistics, Harvard University, Cambridge, MA

Teacher assistant

Courses at Columbia University:

📘(2021, 2022) STAT 6102: Applied Statistics II (PhD level)

📘(2021) STAT 4206 / 5206: Statistical Computing and Introduction to Data Science (undergraduate and masters level)

📘(2019, 2020) STAT 6701: Foundations of Graphical Models (PhD level))

📘(2019) STAT 4204/5204: Statistical inference (undergraduate / masters level)

📘 (2018) STAT 4206: Statistical Computing and Introduction to Data Science (undergraduate level)

📘 (2018) STAT 5224: Bayesian Statistics (masters level)

📘 (2017) STAT 4205: Linear Regression Models (undergraduate level)