4-Neighbor Bootstrap Percolation in King’s Graphs
Talk, George Washington University, Washington, DC, USA
For a complete list of talks I've given, see my CV.
I love giving undergrad-friendly talks about math puzzles and interests. If you'd like me to give a talk, reach out!
Talk, George Washington University, Washington, DC, USA
Talk, University of Michigan, Ann Arbor, MI, USA
A five-minute lightning talk with some of my thoughts on how generative AI can interact with students’ learning. The day before the SEISMIC 2.0 Kickoff, many of us gathered to share our thoughts on generative AI contributes to our goal of implementing equity-based assessment in large-enrollment STEM courses. You can read more of my thoughts about SEISMIC 2.0 here, in the June 2026 SEISMIC Voices entry.
Talk, Penn State New Kensington, New Kensington, PA, USA
Talk, Joint Mathematics Meeting, Washington, DC, USA
This is my favorite talk to give. I updated the slides for my first Joint Mathematics Meeting (JMM) conference so I could present it in Spectra’s Special Session on Research by LGBTQ+ and Allied Mathematicians.
Talk, Marist University Department of Mathematics, Poughkeepsie, NY, USA
In which I adapt the solution to a Stack Exchange problem to be approachable to a general undergraduate math major audience, filling in the gaps to introduce tensor products, norms, generating functions, and Stirling’s Formula.
Talk, Fairmont State University, Fairmont, WV, USA
In which I present my paper on the Inverse Quotient Rule. Using a motivating example from Stewart’s Essential Calculus, Early Transcendentals (2nd edition), I derive a Quotient Rule version of Integration by Parts (which is itself derived from the Product Rule), show additional examples, and give my opinion on why the technique is not commonly taught.
Talk, Westminster College, New Wilmington, PA, USA
In which I introduce a puzzle involving the use of Minecraft’s pistons, which can push blocks but not pull them, where the objective is to push a specific block to a specific location, and where the context of the puzzle values a quick and correct solve, rather than a traditionally “optimal” solve, which might involve using the fewest number of moves.