Teaching Philosophy

I view teaching as an opportunity to equip students with tools for understanding the world—and changing it. Whether introducing foundational sociological theories or guiding students through causal inference and machine learning, I aim to help students connect rigorous methodology with social inquiry.

My goal is to inspire students to use sociology’s analytical power while building strong data science skills. I foster an engaging and inclusive learning environment where students:

Learning Goals

Theory + Methods

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

Critical Inquiry

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

Applied Insight

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

Tutorials

An introduction to causal inference using g-methods, implemented in R. This tutorial series covers the two major approaches for handling time-varying confounders based on G-formula: G-computation and Marginal Structural Models (MSM). Each post walks through a specific technique using clear, reproducible code. More on my GitHub.