Teaching

Teaching philosophy

I am interested in helping students develop statistical reasoning rather than simply memorize statistical procedures. My teaching emphasizes understanding why statistical methods work, connecting methods to real research questions, and using R to explore data.

Courses at the University of Pittsburgh

BIOST 2041: Introduction to Statistical Methods

Graduate | Primary Instructor | 2023–present

Introductory biostatistics course for graduate students in public health.

BIOST 2141: Biostatistical Methods

Formerly BIOST 2039

Graduate | Primary Instructor | 2023–present

Graduate-level biostatistics course covering foundational statistical methods for public health students.

BIOST 2142: Applied Regression Analysis

Formerly BIOST 2049

Graduate | Primary Instructor | 2024–present

Applied regression course emphasizing the use and interpretation of regression methods in public health research.

PUBHLT 0413: Data to Prediction: Machine Learning for Public Health

Undergraduate | Primary Instructor | 2026

Undergraduate course introducing machine learning methods for public health. The course emphasizes practical model building, comparison of classification methods, model evaluation, and interpretation of predictive models.

Earlier teaching

Before joining the University of Pittsburgh, I taught and assisted with graduate biostatistics courses at Johns Hopkins University.

Courses included:

Statistical Methods in Public Health I, II, and III
Methods in Biostatistics II
Data Analysis Workshop I
Advanced Methods in Biostatistics III and IV

I served as a lead teaching assistant and lab instructor for several of these courses.

Other teaching

I have also given guest lectures for PUBHLT 0100: Fundamentals of Public Health at the University of Pittsburgh across multiple semesters.