• Instructor, Engineering Applications of Operations Research (ENGRI 1101, Fall 2019): I again taught ENGRI 1101, continuing to revamp the course and add data science content. My overall instructor rating was a 4.92/5. Here is the syllabus for more information.
  • Instructor, ORIE Project (ORIE 4999, Spring 2019): Over the semester, a team of students wrote a mini-textbook on the decision sciences for ~600 people.  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 4.80/5. For more information, see my syllabus.
  • 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 UdellDavid Shmoys, and Sid Banerjee, I organized and moderated a reading class on mathematical techniques for optimization; see the syllabus.

Canada/USA Mathcamp

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.