Teaching

I am highly enthusiastic about teaching and supervising students on different levels, and I have experience in both substantive and methodological subjects. I have been a teaching assistant for substantive courses on IR and IPE. Currently, I am teaching as the solo instructor for an undergraduate-level quantitative methods course at Penn State University.

At the undergraduate level, I am excited to teach both introductory and advanced classes in International Relations, including Intro to IR, International Political Economy, and Trade and Supply Chain Politics. Methodologically, I am prepared to teach courses on research design, quantitative analysis, (advanced) text analysis, and deep learning.

PLSC 309: Quantitative Political Analysis introduces students to the foundational tools of empirical research in political science. The course teaches students how to turn complex political phenomena into measurable data, visualize and analyze those data using statistics, and draw valid inferences about real-world issues. Students learn how political scientists use quantitative evidence to test theories, evaluate claims, and communicate results to academic and public audiences. The class emphasizes hands-on learning with R and RStudio, regular lab assignments, and an original research project that culminates in a professional research poster.

• Software: R and RStudio
• Major Assignments: Lab reports, two exams, independent research project, and in-class poster presentation

Topics include:

• Turning political concepts into data, measurement, and operationalization
• Research design, causal inference, and hypothesis generation
• Descriptive statistics: central tendency, dispersion, and data visualization
• Probability, sampling, and the logic of statistical inference
• Hypothesis testing and differences of means
• Correlation and bivariate regression
• Multivariate regression, model specification, and statistical control
• Dummy variables, multicollinearity, and interpretation
• Logistic regression and prediction
• Introduction to Bayesian thinking and uncertainty
• Research ethics, replication, and the data-rich future of social science

Syllabus (Available upon request)