As an applied mathematician, my goal in the classroom is to help students bridge the gap between abstract mathematical theory and real-world application. Whether we are exploring calculus, differential equations, or mathematical modeling, I emphasize active learning, building mathematical intuition through computer simulations, and breaking down complex phenomena into approachable pieces. I strive to foster an inclusive, collaborative environment where students feel empowered to experiment, question, and develop rigorous problem-solving skills.

Courses Taught at Middlebury

  • MATH 0715: Advanced Mathematical Modeling Senior Seminar

    • This course focuses on deterministic and stochastic model building, featuring one-on-one mentoring of course-long, student-chosen research projects. Past projects span epidemiology, mathematical neuroscience, pattern formation, stochastic modeling of wind turbine degradation, and climate/energy systems. This course counts as one of the Applied Math Senior Seminars required to complete the Applied Math Track.

      In the spring of 2026, our class created the first edition of the Middlebury Applied Math Journal! Students in the course contributed 10-page journal articles complete with autobiographies outlining their journey through mathematics.

  • MATH 0326: Partial Differential Equations
    • This course covers first and second-order linear PDEs, studying the Laplace, heat, and wave equations using analytical and numerical methods. This course counts as one of the Advanced Differential Equations electives along the Applied Math Track.
  • MATH 0315: Mathematical Modeling
    • This course teaches discrete, continuous, and probabilistic approaches to modeling using motivating examples across population dynamics, epidemiology, and neuroscience. This course counts as one of the Advanced Differential Equations electives along the Applied Math Track.
  • MATH 0228: Introduction to Numerical Analysis
    • In this course, students learn the development, analysis, and implementation of numerical methods for approximating solutions, interpolation, rootfinding, and numerical ODEs using MATLAB. This course counts as one of the Computational electives along the Applied Math Track.
  • MATH 0226: Differential Equations
    • This course is an introduction to ordinary differential equations (ODEs) using analytical, qualitative, and numerical techniques. This is the first course required along the Applied Math Track and introduces students to programming using MATLAB.
  • MATH 0200: Linear Algebra
    • This course covers matrices, systems of linear equations, vector spaces, independence, orthogonality, linear transformations, eigenvalues, and determinants.
  • MATH 0122: Calculus II
    • This course covers techniques of integration, improper integrals, applications of integrals, infinite series, Taylor’s theorem, and polar coordinates.

Undergraduate Research & Mentorship

Mentoring undergraduate researchers is a central and rewarding part of my work. I regularly involve undergraduate students in computational neuroscience and applied mathematics projects funded through grants like the MAA NREUP Award and institutional programs.

Are you a student interested in undergraduate research?

  • Projects typically involve developing differential equation models, running computer simulations (e.g., in MATLAB or Python), and analyzing model-generated data related to neural dynamics or pain modulation.
  • Ideal background includes coursework in Differential Equations (MATH 0226) and some prior coding experience.
  • If you are a Middlebury student interested in research opportunities or senior thesis advising, please feel free to reach out via email or drop by my office hours!

Office Hours & Location

  • Location: Warner Hall 204, Middlebury College
  • Contact: jcrodelle [at] middlebury [dot] edu

For current course office hours or to schedule an appointment, please check your course’s syllabus page or contact me directly via email.