Statistics is the backbone of experimental and observational science, and as such it appears in most scientific and quantitative fields. But statistics is hard. With colleagues, I try to distill and communicate important concepts, and develop pedagogically sound papers, notebooks, software and courses. Currently I’m writing chapters for the upcoming textbook Bayesian Workflow.
My work as a teacher falls into two categories: I teach one or two days workshops on modeling and data analysis targeted at audiences from various backgrounds, including biomedicine, physics, and political science. My courses cover fundamental concepts in Bayesian statistics 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.
I’ve also been a teacher assistant for five consecutive years for courses taught by Columbia’s department of statistics, at all levels (undergraduate, masters and PhD).
I’ve been invited as an intrusctor for the following courses and conferences:
- (2018 – 2022) Introduction to Probability and Bayes, PHC 506: Biometry in Pharmaceutics, University of Buffalo, School of Pharmacy, Buffalo, NY. [notes]
- (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
Courses at Columbia:
- (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 Grapical 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)
- (2017) Stan for Physics, Massachusetts Institute of Technology, Cambridge, MA (5 days workshop, taught by Michael Betancourt)
- (2017) Getting Started with Bayesian PKPD Modeling using Stan and Torsten, Population Approach Group in Europe 26, Budapest, Hungary (1 day workshop, taught by Bill Gillespie)
- (2016, 2017) Getting Started with Bayesian PKPD Modeling using Stan, American Conference on Pharmacometrics 6 and 7 (1 day workshop, taught by Bill Gillespie)