- Instructor, Engineering Applications of Operations Research (ENGRI 1101, Fall 2019): I’ll be teaching ENGRI 1101 again this fall! Here is the draft syllabus.
- Instructor, ORIE Project (ORIE 4999, Spring 2019): Over the semester, we’ll be writing a mini-textbook on the decision sciences for several hundred participants. This project-based course meets the technical communication requirement through Writing Intensive Opportunity: Practicum in Technical Writing (ENGRC 3023).
- Instructor, Engineering Applications of Operations Research (ENGRI 1101, Fall 2018): I taught the ORIE department’s 88-student introductory course for first-year engineering students. I added material on social networks, social choice, racial gerrymandering, and statistics (linear regression and ecological inference), and emphasized real-world applications. My overall instructor rating was a 4.80, compared to a 3.67 average within ORIE. For more information, see my syllabus and evaluations (minus two pages of comments on my incredible TAs).
- Teaching Assistant, Topics in Linear Optimization (ORIE 5311, Spring 2017, First Half-Semester): Gave weekly lectures to Master’s level students introducing integer programming. I earned an average overall quality rating of 5/5.
- Co-organizer and Moderator, Mathematical Techniques for Optimization Reading Course (ORIE 7390, Fall 2017): With Madeleine Udell, David Shmoys, and Sid Banerjee, I organized and moderated a reading class on mathematical techniques for optimization; see the syllabus.
During the summers of 2015-2017, I taught several week-long courses at the Canada/USA Mathcamp. It’s a teachers paradise: wonderful, motivated students, colleagues who love to bounce teaching ideas, and the opportunity to teach basically whatever content — and in whatever style — you’re excited about. Some of my favorite courses include:
- Statistical Modeling: an intensive course illustrating the statistical modelling process through the general linear model. Students got a taste of statistical theory during lectures and learned R to analyze data during labs.
- Not Your Grandparents’ Algorithms Class: an algorithms class for pure mathematicians. This course motivated and discussed theory pertinent to mathematically beautiful algorithms, including the ellipsoid algorithm, branch and bound, and multiplicative weights.
- The Traveling Salesman Problem: Recent Breakthroughs: In five days, we built up the optimization background to motivate and work through the technical details from this recent paper.
- A Crash Course in Axiomatic Probability: a fun course building probability from nitty-gritty axiomatic results through the Weak Law of Large Numbers.
- Turing and His Work: a seminar where we “got to know” Alan Turing by reading his work, including his work on the Turing test, chess, and Turing machines.
- The Development of Probability: a course exploring how and why probability developed during the 17th century. We discussed some of the earliest tools, limit theorems, and applications of probability.