trevorkarn.github.io

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trevor karn

about

i’m a mathematics phd student at the University of Minnesota advised by Vic Reiner

my research interests are in algebraic combinatorics and applied topology

my pronouns are he/him

publications

Topological Structure is Predictive of Deep Neural Network Success in Learning (with Christopher Griffin and Benjamin Apple), submitted

Equivariant Kazhdan-Lusztig theory of paving matroids (with George Nasr, Nicholas Proudfoot, and Lorenzo Vecchi). to appear in Algebraic Combinatorics (code) (slides)

Modeling a Hidden Dynamical System Using Energy Minimization and Kernel Density Estimates (with Steven Petrone and Christopher Griffin), Physical Review E 100 (2019)

Stirling numbers in braid matroid Kazhdan–Lusztig polynomials (with Max D. Wakefield), Advances in Applied Mathematics 103 (2019)

teaching

i have lectured the following classes at UMN:

i have t.a.’d the following classes at UMN:

open source development

i’m a fan of python. in particular, sagemath. since 2020 i have been contributing to sagemath. here are some things i’ve done in sagemath.

in summer 2021 i participated in the Google Summer of Code with sagemath. i did it again in 2022.

talks

click here to see more about some of my talks.

notes

click here to see some miscellaneous notes.

combinatorics seminar

i helped to organize the 2020 UMN student combinatorics seminar. here is the schedule of speakers that year.