Teaching
Teaching philosophy
I organize my teaching around three goals: helping students understand statistical theory, put that theory into computational practice, and develop the critical perspective needed to evaluate an analysis. In the current era of statistical machine learning and AI, students need to understand not only how to fit models, but when predictions are reliable, how uncertainty should be communicated, and how modeling assumptions shape scientific conclusions.
I use reproducible R and Python notebooks, simulations, real scientific data, and projects that require students to make and defend modeling decisions. When AI tools are appropriate, I ask students to document their use, verify outputs, and remain accountable for every claim and line of submitted code. This makes reasoning, validation, and reproducibility visible parts of the learning process.
Courses taught
University of Connecticut
Instructor, Department of Statistics
- STAT 4195/5095 - Special Topics: Deep Learning
- STAT 2225 - Introduction to Statistical Programming (Python)
Texas A&M University
Instructor, Department of Statistics
- STAT 211 - Principles of Statistics I
- STAT 438 - Bayesian Statistics
- STAT 335 - Principles of Data Science
- STAT 421 - Machine Learning
- STAT 600 - Reproducible Computational Statistics
University of Illinois Urbana-Champaign
Teaching Assistant, Statistics and Accounting
- STAT 400 - Statistics and Probability I
- ACCY 570 - Data Analytics Foundations for Accountancy
- ACCY 571 - Statistical Analyses for Accountancy
Teaching interests
I am prepared to teach across the statistics and data science curriculum, including statistical learning, machine learning, Bayesian statistics, computational statistics, deep learning, uncertainty quantification, and spatiotemporal or environmental data science. At the graduate level, I am especially interested in courses that connect rigorous statistical methodology with modern machine learning and scientific applications.