Current teaching at Cornell

Causal Inference (undergraduate, co-taught with Sam Wang).

Studying Social Inequality with Data Science (undergraduate)

Causal Inference in Observational Settings (PhD seminar)

Past: I have taught graduate and undergraduate statistics and data science for social scientists.

A handout I designed for a course on generalized linear models.

My teaching experience is in a statistics sequence aimed at Ph.D. students in sociology, cross-listed for undergraduates.

I was a teaching assistant in the full two-semester sequence under Brandon Stewart in the 2016–2017 academic year. I returned to TA the second course of the sequence in spring 2018 under Matthew J. Salganik. In each course, I led a 2-hour section reviewing materials from the course every other week. (link to sample handout on generalized linear models) (links to sample slides on random variables, likelihood inference, binary outcome modelsduration models, and missing data)

Course evaluations have been universally positive.

"Ian did a particularly great job tying class material to social science research."

"He was good at deconstructing theoretical concepts into real world examples that we could appreciate and understand."

"Ian is great at providing intuitive examples and explanations of complex topics."