• Instructor, ORIE Project (ORIE 4999, Spring 2019): This spring I’m thrilled to be running a project-based course for a small team of Cornell undergraduates!  This course will fulfill Cornell Engineering’s technical communication requirement  through Writing Intensive Opportunity: Practicum in Technical Writing (ENGRC 3023).  Over the semester, we’ll be writing a mini-textbook on political math (voting theory, apportionment, and gerrymandering) for several hundred inmates in NY State Prisons.
  • 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 within ORIE. For more information, see my syllabus.  (Evaluations, minus the two pages of comments on my incredible TAs.)
  • Teaching Assistant, Topics in Linear Optimization (ORIE 5311, Spring 2017)
    I gave weekly lectures to Master’s level students in an intense, half-semester class.  These lectures introduced integer programming.  I also wrote final exam problems and weekly optional practice problems on this material.  I earned an average overall quality rating of 5/5. (Evaluations.)
  • Co-organizer and Moderator, Mathematical Techniques for Optimization Reading Course (ORIE 7390, Fall 2017): With Madeleine UdellDavid Shmoys, and Sid Banerjee, I organized a pass/fail reading class on mathematical techniques for optimization based on a mix of secondary sources and papers.  Topics included hierarchies in optimization and online convex optimization.  More details can be found in the 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.