Papers & interests

Research

My current interests are in combinatorics and algebraic geometry.

My previous work includes algebraic combinatorics, mathematical modeling, and reinforcement learning.

Preprints

  1. 2026

    The Terwilliger algebra for the distance-regular graphs with valency three

    Kevin Kauflin, Paul Terwilliger, Barnabás Valkó, Jimmy Vineyard, and Hanyi Wu

    arXiv:2606.05393 · June 2026

    About this paper

    We study the Terwilliger algebras of the 13 distance-regular graphs of valency three, decomposing their standard modules into irreducible modules and determining the dimensions of the algebras.

Publications

  1. 2025

    Concrete billiard arrays of polynomial type and Leonard systems

    Jimmy Vineyard

    Linear Algebra and its Applications 710, 296–309.

    About this paper

    I construct concrete billiard arrays from the eigenspaces of a multiplicity-free linear map and describe how their boundaries relate to the split decompositions associated with a Leonard system. This work grew out of my senior honors thesis with Paul Terwilliger.

  2. 2025

    The Dynamics of Mood in Bipolar Disorder: How Mathematical Models Help Phenotype Individuals, Forecast Mood, and Clarify Underlying Mechanisms

    Amy L. Cochran and Jimmy Vineyard

    Current Psychiatry Reports 27, 687–696.

    About this paper

    A review of mathematical approaches to describing mood dynamics in bipolar disorder, with an emphasis on how models can characterize individuals, forecast changes in mood, and help examine underlying mechanisms.

  3. 2025

    Integrating Local Learning into the Two-Stage Markov Task to Separate Model-Based from Model-Free Learning

    Peizhe Li, Jimmy Vineyard, Seungyeon Oh, and Jack Maloney

    SIAM Undergraduate Research Online 18.
    Project advisors: Haley Colgate Kottler and Amy Cochran.

    About this paper

    We propose a local-learning phase for the two-stage Markov task and evaluate it with simulations, aiming to better distinguish model-based from model-free learning.