Teaching

Bucknell

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

  • CSCI 204: Data Structures and Algorithms, Fall 2020 and Fall 2021 (with associated labs in Fall 2020, Fall 2021, and Fall 2024)
  • CSCI 311: Algorithms & Analysis, Fall 2023 and Fall 2024 (with associated recitations in Fall 2023 and Fall 2024)
  • FOUN 096: Winning with Math (a first-year foundation seminar), Fall 2021, Fall 2023, and Fall 2024
  • Math 216: Statistics 1, Spring 2022, Spring 2024, and Spring 2025
  • Math 240: Applied Combinatorics, Spring 2021
  • Math 241: Discrete Structures (introducing proofs, combinatorics, and graph theory for an audience primarily of CS majors), Spring 2021 [Note: Math 240 is a half-credit course that meets for half of Math 241].

I have also ran a variety of individual and group independent studies under CSCI 278 and CSCI 378, including

  • CSCI 378: Data Science & Decision Making (applied data science work, for instance on course enrollment forecasting at Bucknell and final exam scheduling), Spring 2022, Spring 2023, Fall 2023, and Fall 2024
  • CSCI 278: Engineering Ingenuity (a grab-bag project, which included a student building a fully-programmable guitar pedal [second story on that page] and analyzing StockX data), Spring 2023
  • CSCI 378: Research in Theoretical CS (research on the Traveling Salesman Problem), Spring 2022, Fall 2022, and Spring 2024.  See this story  and this paper for some of the student’s work.
  • CSCI 378: 3D Printing for CS Education (developing and printing tools to teach introductory CS), Spring 2022, Fall 2022, and Spring 2023
  • CSCI 378: Data Science Development (an exploratory project on Neural Networks), Fall 2022
  • CSCI 378: Modelling & Simulation (research forecasting NFL Scorigami’s.  See this paper), Spring 2023 and Spring 2024
  • CSCI 378: Formula 1 Analytics (simulation research on Formula 1 race strategy), Spring 2023
  • CSCI 378: Data Analytics of Broadway (research related to the financing of Broadway shows), Fall 2023
  • CSCI 278: Soccer Analytics (data research related to Soccer), Fall 2024
  • CSCI 378: Hockey Analytics (data research related to Hockey), Fall 2024
  • CSCI 278: Digital Music Production (a grab-bag project exploring data related to music, digital music production, and building a guitar pedal), Spring 2025

Cornell

  • 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.

Canada/USA Mathcamp

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.