Teaching

Courses for which I have been the lead instructor at CU Boulder:

• GEOG 3023, Statistics and Geographic Data – Fall 2025, Spring 2026
  • Course description: This is an introductory course in statistical and computational thinking with a special emphasis on problems in the geographical sciences. This course consists of two weekly lectures and a lab section. The objectives of this course are to:
  1. Develop “statistical literacy,” a working understanding of statistics that can help you to critically evaluate data-driven results in the discipline of Geography (and beyond).
  2. Obtain a basic set of statistical tools for data analysis, with an understanding of how to choose which tool to use, how to implement in statistical software, and how to interpret the results.
  3. Use R to make graphs and maps, calculate descriptive statistics, conduct hypothesis tests about sample means, and run (valid) linear regressions.
  4. Understand problems arising from the use of spatial data.

Courses for which I was a teaching fellow at the Harvard T.H. Chan School of Public Health:

• BST 226, Applied Longitudinal Analysis – Spring 2024

  • Course description: This course covers modern methods for the analysis of repeated measures, correlated outcomes and longitudinal data, including the unbalanced and incomplete data sets characteristic of biomedical research. Topics include an introduction to the analysis of correlated data, analysis of response profiles, fitting parametric curves, covariance pattern models, random effects and growth curve models, and generalized linear models for longitudinal data, including generalized estimating equations (GEE) and generalized linear mixed effects models (GLMMs).

• BST 210, Applied Regression Analysis – Fall 2023

  • Course description: Topics include model interpretation, model building, and model assessment for linear regression with continuous outcomes, logistic regression with binary outcomes, and proportional hazards regression with survival time outcomes. Specific topics include regression diagnostics, confounding and effect modification, goodness of fit, data transformations, splines and additive models, ordinal, multinomial, and conditional logistic regression, generalized linear models, overdispersion, Poisson regression for rate outcomes, hazard functions, and missing data. The course provides students with the skills necessary to perform regression analyses and to critically interpret statistical issues related to regression applications in the public health literature.

• BST 222, Basics of Statistical Inference – Fall 2021

  • Course description: This course provides a basic yet thorough introduction to the probability theory and mathematical statistics that underlie many of the commonly used techniques in public health research. Topics covered include probability distributions (normal, binomial, Poisson), means, variances and expected values, finite sampling distributions, parameter estimation (method of moments, maximum likelihood), confidence intervals, hypothesis testing (likelihood ratio, Wald and score tests). All theoretical material are motivated with problems from epidemiology, biostatistics, environmental health and other public health areas. This course is aimed towards second year doctoral students in fields other than Biostatistics. Background in algebra and calculus required.