At the University of Virginia, I teach several methods courses in the Department of Politics, including the core graduate-level sequence in statistics. Below are short descriptions for the courses. Syllabi are available upon request.

PLAD 8500: Statistical Measurement. Graduate. Covers challenges that political scientists face in measuring unobservable concepts and the ways to overcome them. It briefly introduces measurement theory and then reviews the recent advances in literature on measurement methods for political science.

PLAD 8320: Maximum Likelihood Estimation. Graduate. Consists of two parts that cover, respectively, the theory of maximum likelihood estimation and the practical application of generalized linear models in political science. Specific examples include binary, ordered, multinomial, and count models.

PLAD 8310: Linear Regression. Graduate. Focuses on classic linear regression and the least-squares estimation method. An important topic is interpretation of regression results with a corresponding review of statistical inference, testing, and interval estimation.

PLAD 7100 101: Mathematics for Political Science. Graduate. Reviews mathematical concepts and techniques to prepare first-year graduate students for the core sequence in statistics. It starts from the review of high-school level mathematics and then introduces students to calculus and linear algebra.

PLAD 2222: Research Design in Political Science. Undergraduate. An introduction to research methods and quantitative analysis for political science. It covers theory construction, posing proper research questions, hypotheses formulation, and issues related to causal inference

I also had teaching appointments at the University of Michigan and the Higher School of Economics (Russia).