I teach analytics and machine learning to undergraduate business students. My aim is that they leave able to judge when an analytical approach genuinely improves a decision and when it quietly misleads, not simply how to run the method.
Courses
Business Analytics and Modeling (BUS K353) — Kelley School of Business, Indiana University
Instructor, Spring 2024, 2025, and 2026 · three sections per year
- An undergraduate analytics and machine learning course built around a layered progression: descriptive analytics to characterize data, predictive analytics to anticipate outcomes, and prescriptive analytics to choose among actions.
- Students work in R on real business problems rather than pre-cleaned teaching datasets.
- Topics include data analysis and visualization, parametric and non-parametric supervised learning, unsupervised learning, Monte Carlo simulation, and optimization.
Economic Decision Making (IOE 201) — Industrial and Operations Engineering, University of Michigan
Instructor, Fall 2020 · core undergraduate course, in-person and online sections
Introduction to Markov Processes (IOE 316) — University of Michigan
Recitation Instructor, Winter 2020
Teaching Recognition
2026
Finalist, INFORMS Junior Faculty Interest Group (JFIG) Teaching Excellence Award
2026
Finalist, INFORMS Data Mining Society Teaching Award
2022
Winner, Rackham Outstanding Graduate Student Instructor Award University-wide award, Rackham Graduate School, University of Michigan
2020
Winner, Joel and Lorraine Brown Graduate Student Instructor of the Semester Industrial and Operations Engineering, University of Michigan
Mentoring
Undergraduate
Faculty Mentor, Center of Excellence for Women & Technology, Indiana University
Graduate
Faculty Mentor, Faculty Assistance in Data Science (FADS) Program, Kelley School of Business