|Fall 2020||STATS 250: Introduction to Statistics and Data Analysis||Jack Miller, Ph.D.||Graduate Student Mentor, Graduate Student Instructor|
|Fall 2019, Winter 2020||STATS 250: Introduction to Statistics and Data Analysis||Brenda Gunderson, Ph.D.||Graduate Student Mentor, Graduate Student Instructor|
|Spring 2018||STATS 250: Introduction to Statistics and Data Analysis||Brenda Gunderson, Ph.D.||Graduate Student Instructor|
|Winter 2018||STATS 415: Data Mining||Liza Levina, Ph.D.||Graduate Student Instructor|
|Fall 2017||STATS 500: Statistical Learning I: Regression||Brian Thelan, Ph.D.||Graduate Student Instructor|
|2018, 2019||Introduction to the R Statistical Computing Environment||John Fox, Ph.D.||Teaching Assistant|
|2018, 2019||Multilevel Models I: Introduction and Application||Mark Manning, Ph.D.||Teaching Assistant|
|Spring 2012||BIOS 40411: Biostatistics||Gary Lamberti, Ph.D.||Undergraduate Teaching Assistant|
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.