Teaching

Below is a list of courses for which I’ve served as a teaching fellow.

EPI207 - Advanced Epidemiologic Methods

Instructor: James Robins

Terms: Fall 2020, Fall 2021 (Lead)

Required course for epidemiology PhD students. Causal inference for time-varying exposures: g-formula, inverse-probability weighting, marginal structural models, static and dynamic treatment regimes. Responsible for leading 90 min lab section and grading homeworks and tests.

  • Slides and code for lab on parametric g-formula.

🏆 Department of Epidemiology Excellence in Teaching Award 2021

PHS2000 - Quantitative reseach methods

Instructors: Tyler Vanderweele, Michael Hughes, Issa Dahabreh, and Jarvis Chen

Terms: Fall 2019, Spring 2020, Fall 2021

Year-long required methods course for first-year PhD students. Regression models, sampling, longitudinal and multilevel analysis, time-varying confounding, mediation and interaction, econometric methods, and missing data. Responsible for leading 90 min lab section, developing homework assignments and tests, and drafting course materials.

  • Slides for bootcamp review on inference.

  • Slides and code for bootcamp review on linear regression.

  • Slides and code for lab on survival analysis.

  • Handout and code for lab on sensitivity analysis.

  • Handout and code for lab on causal interaction and effect modification.

  • Handout and code for lab on mediation.

  • Handout and code for lab on the bootstrap.

🏆 GSAS Distinction in Teaching Award 2019, 2020, and 2021

EPI260 - Mathematical Modeling of Infectious Diseases

Instructor: Marc Lipsitch

Terms: Spring 2023

Dynamical models to study the transmission dynamics of infectious diseases. Design and construction of appropriate differential equation models, equilibrium and stability analysis, parameter estimation from epidemiological data, determination and interpretation of the basic reproductive number of an infection, stochastic and deterministic models, heterogeneity, techniques for sensitivity analysis, and critique of model assumptions.