Teaching

Information about my Bucknell courses is primarily through Moodle. If you’d like more details or a syllabus, just ask!

In the 2022-23 academic year, I will be on my Untenured Faculty Leave. I will be running independent studies on CSCI 378: Research in Theoretical CS and CSCI 378: Data Science Development.
In the 2021-22 academic year, I taught CSCI 204: Data Structures and Algorithms, a foundation seminar entitled Winning with Math, and MATH 216: Statistics I. I ran independent studies on CSCI 278: 3D Printing and Design: Instantiating Data Structures, CSCI 378: Research in Theoretical CS, and CSCI 378: Data Science & Decision Making.
In the 2020-21 academic year, I taught CSCI 204: Data Structures and Algorithms and Math 241: Discrete Structures (introducing proofs, combinatorics, and graph theory for an audience primarily of CS majors).

Instructor, Engineering Applications of Operations Research (ENGRI 1101, Fall 2018-9): The ORIE department’s ~100-student introductory course for first-year engineers, surveying broad ideas in Operations Research. I added content on social networks, social choice, racial gerrymandering, statistics (linear regression and ecological inference), and clustering. 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).
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. 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.
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.