Teaching

Spatial Data Analysis (Spring 2026)

• Shapely points, pandas/geopandas, CRS transforms
• Shapely geometries, boolean logic, spatial joins
• Rasters
• Representations of vector fields, spatial aggregates
• Point processes
• Regressions using raster data, point processes intensity prediction
• Spatial lag models, spatial fixed effects
• Spatial autocorrelation, spatial cross-validation

PhD Probability and Statistics (Fall 2022)

• Sigma Algebras and Independce
• Integration
• Conditional Expectations and Conditional Sigma Algebras
• Probability and Distribution Limits
• MLE and the Invariance Property

PhD Applied Econometrics (Fall 2022)

• Why We Love OLS
• Rubin Causal Model & SUTVA

PhD Probability and Statistics (Fall 2021)

• Conditional Expectations and Characteristic Functions
• Distributions of Multivariate Transformations and Convergence of Combinations of Sample Statistics
• Exponential Families, Identification, and MLE
• MLE with a closed parameter space, GMM, and the finite sample distribution of MLE.
• Asymptotic distribution of estimators and hypothesis testing.
• Efficient GMM, Proving Statistics are Complete, UMVUEs, and the Cramer-Rao Lower Bound

PhD Applied Econometrics (Fall 2021)

• Rubin Causal Model & SUTVA
• The Regression Adjustment Design
• Non-parametric Regression and Selection on Observables Designs
• Big-Picture Takeaways from Selection on Observables
• Recent Advances in Diff-in-Diff
• Making Instruments from Known Exogeneity
• Spatial Regression Discontinuity Designs