Teaching Philosophy

I view teaching as an opportunity to help students make sense of society through analytical reasoning and methodological rigor. Whether introducing foundational sociological theories or guiding students through causal inference and machine learning, I aim to connect substantive questions with appropriate tools. To that end, I encourage students to:

Bridge Theory and Methods

  • Learn quantitative methods and machine learning while grounding their analysis in sociological theory and frameworks.

Practice Critical Inquiry

  • Sharpen their thinking about social stratification and inequality through data-driven investigation and interpretation.

Develop Applied Insight

  • Apply advanced analytical skills to address policy challenges, transforming research into actionable insight.

Exemplary Teaching Materials

This tutorial series introduces core concepts in causal inference through the lens of g-methods, implemented in R. Designed for students and researchers in the social sciences, the series focuses on two key strategies for addressing time-varying confounding: G-computation and Marginal Structural Models (MSMs). Each post integrates sociological reasoning with hands-on analytical tools, walking through methods using reproducible code and real-data examples. Materials aim to support students in bridging theory and method while developing applied insight. Full materials are available on GitHub.