Teaching
Teaching Philosophy
The best thing about being a statistician is that you get to play in everyone’s backyard.
The (probably overused) above quote from John Tukey captures not only what I love most about statistics, but also my approach to teaching undergraduate statistics. My teaching seeks to connect statistics to students’ lives and interests, and I am focused on helping them understand the bigger picture of their work. In an introductory course, this involves having students work together and think deeply about what conclusions they can and cannot draw from their analyses, and the non-statistical impact those conclusions might have. Similarly, in an upper-level undergraduate data mining course, I encouraged students to think carefully about tradeoffs between model performance and interpretability, and brought in news stories highlighting ethical issues in data science.
Over summer 2020, I worked with Jack Miller, Ph.D., to redesign the University of Michigan’s introductory statistics course (STATS 250) to focus on simulation-based inference, and to move labs and other activities to more deeply integrate R via RStudio. Through this work, I’m able to make a strong impact on how we engage learners in statistics, and get them excited about a course they may have been told to fear. Concurrently, I am working with other graduate students at Michigan to develop a mentorship program for Graduate Student Instructors to focus on evidence-based and inclusive teaching strategies to better cultivate the next generation of statisticians.
Courses Taught
Primary instructor with Trang Q. Nguyen
8-week hour-long seminar for masters and doctoral students in mental health. The topic was “Promises and Pitfalls of Prediction Models in Mental Health”.
Graduate Student Instructor
Large, non-calculus-based, cross-disciplinary introductory statistics course. Taught 2-3 weekly lab sessions of 30 students each.
Course Instructors: Jack Miller, Ph.D.; Brenda Gunderson, Ph.D.
Teaching Assistant
Four-week project-based course for political and social scientists interested in mixed modeling. Held daily office hours to assist students with project-based learning.
Course Instructor: Mark Manning, Ph.D.
Teaching Assistant
Two-week lecture series on graphics, data management, modeling, etc., in R. Held daily office hours.
Course Instructor: John Fox, Ph.D.
Graduate Student Instructor
Upper-level undergraduate introductory machine learning course using An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani). Taught weekly lab session for approximately 45 students.
Course Instructor: Liza Levina, Ph.D.
Graduate Student Instructor
First graduate-level regression course for Applied Statistics masters students, using Linear Models with R, 2nd ed. (Faraway). Held weekly office hours and graded homework and exams.
Course Instructor: Brian Thelan, Ph.D.
Undergraduate Teaching Assistant
Senior undergraduate-level introductory biostatistics course for biology and life science majors. Co-taught weekly lab sessions with a graduate TA, graded lab reports.
Course Instructor: Gary Lamberti, Ph.D.