Overview

Teaching Activities & Philosophy

My teaching spans undergraduate and graduate courses in statistics. I emphasize clear learning outcomes, alignment between instruction and assessment, and the integration of data-driven, reproducible workflows. I design courses to balance conceptual understanding and practical data skills for academic and professional contexts.

Current Courses

  • STAT-S 631 & STAT-S 632 — Applied Linear Models I and II
    A two-semester sequence for graduate students focused on studying, implementing, and interpreting linear models using inferential techniques, matrix-based methods, and R.

  • STAT-S 431 & STAT-S 432 — Applied Linear Models I and II
    The undergraduate equivalent to STAT-S 631 & STAT-S 632.

  • STAT-S 350 — Applied Linear Models I
    A self-contained, rigorous introduction to statistical inference for students seeking a deeper understanding of statistical reasoning.

  • STAT-S 519 — Introduction to Statistics in R
    An online introductory statistics course for graduate students covering probability foundations, estimation/testing, regression, and reproducible workflows.

  • STAT-S 530 / STAT-S 330 — Biometry (graduate / undergraduate)
    Applied second course in biometry emphasizing modeling, implementation in R, and scientific communication.

Former Courses

  • STAT-S 520 — Introduction to Statistics
  • STAT-S 352 — Data Modeling and Inference
  • STAT-S 303 — Statistics for the Life Sciences
  • STAT-S 303 — Statistics for Speech and Hearing Sciences
  • STAT-S 100 — Statistical Literacy
  • STAT-S 301 — Statistics for Business
  • STAT-S 681 — Topics in Statistical Machine Learning
  • STAT-S 695 — Reading in Statistics

For Students

Links to syllabi, assignments, labs, and course GitHub repositories will be added here. Upload files to /assets/files/ and link them from this page (I can help wire those links once files are uploaded).

Curriculum & Development

  • Curricular redesign of STAT-S 520 and STAT-S 631
  • Integration of reproducible workflows into existing courses
  • Simulation-first inference approach