In some sense, statistics is the backbone of experimental and observational science. But statistics is (really f***ing) hard, and requires a level of mathematics which is not always available to practitioners. Distilling and communicating important concepts is a bit of an art. Some of my colleagues are very dedicated pedagogues and I try (with various degrees of success) to follow their example.
My teaching experience falls into two categories: I TA courses taught by Columbia’s department of statistics, and teach or TA workshops targeted at specialists, who may not be statisticians.
Guest lecturer
I’ve been invited as a guest speaker for the following courses and conference:
- Probability and Bayes, PHC 506: Biometry in Pharmaceutics, University of Buffalo, School of Pharmacy, Buffalo, NY.
- How to Develop for the Stan C++ Core Language, Stan Conference 2018, Pacific Grove, CA. [slides]
- Introduction to Bayesian Data Analysis with Stan, STAT 220: Bayesian Statistics, Harvard University, Cambridge, MA
Teacher assistant
Courses at Columbia:
- STAT 4206: Statistical Computing and Introduction to Data Science
- STAT 5224: Bayesian Statistics
- STAT 4205: Linear Regression Models
Workshops:
- Stan for Physics, Massachusetts Institute of Technology, Cambridge, MA, (5 days workshop, taught by Michael Betancourt)
- 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)
- Getting Started with Bayesian PKPD Modeling using Stan, American Conference on Pharmacometrics 7, Bellevue, WA, (1 day workshop, taught by Bill Gillespie)
- Getting Started with Bayesian PKPD Modeling using Stan, American Conference on Pharmacometrics 6, Arlington, VA, (1 day workshop, taught by Bill Gillespie)