David A. Edwards joined the Kenyon faculty in 2025 after teaching for nearly 30 years at the University of Delaware. His research focuses on optimizing various aspects of the 3D printing process, as well as studying various products in mathematical finance. In addition, for over 30 years Edwards has worked to solve real-world industrial problems as part of the Mathematical Problems in Industry (MPI) workshop program.

Edwards has supervised nearly 20 research projects, and enjoys advising and mentoring students outside the classroom. In 2018 he received UD’s Outstanding Club Advisor award for his work with the Actuarial Sciences Club.  

Edwards has taught over a dozen different courses with a focus on instructional innovation. His current pedagogical interests include fostering judicious and reflective student use of AI to enhance their learning.

Areas of Expertise

Applied and industrial mathematics, perturbation methods, mathematical finance, actuarial science

Education

1994 — Doctor of Philosophy from California Institute of Technology

1990 — Bachelor of Science from California Institute of Technology

Courses Recently Taught

This course examines differentiation and integration in three dimensions. Topics of study include functions of more than one variable, vectors and vector algebra, partial derivatives, optimization, and multiple integrals. Some of the following topics from vector calculus also are covered as time permits: vector fields, line integrals, flux integrals, curl and divergence. This counts toward the core course requirement for the major. Prerequisite: MATH 112 or a score of 5 on the AB calculus AP exam, or an AB sub-score of 5 on the BC calculus AP exam. Offered every semester.

This course focuses on the study of vector spaces and linear functions between vector spaces. Ideas from linear algebra are useful in many areas of higher-level mathematics. Moreover, linear algebra has many applications to both the natural and social sciences, with examples arising in fields such as computer science, physics, chemistry, biology and economics. In this course, we use a computer software system, such as Maple or Matlab, to investigate important concepts and applications. Topics to be covered include methods for solving linear systems of equations, subspaces, matrices, eigenvalues and eigenvectors, linear transformations, orthogonality and diagonalization. Applications are included throughout the course. This counts toward the core course requirement for the major. Prerequisite: MATH 213. Generally offered three out of four semesters.

Ordinary differential equations (ODEs) arise naturally to model systems that occur in physics, biology, chemistry and economics. This course discusses techniques for finding, analyzing, and interpreting solutions of ODEs using analytic, numerical and qualitative techniques. We discuss first-and second-order ODEs, as well as first-order systems of ODEs. Applications are woven throughout the course. Other topics, as time permits. This course counts toward the computational/modeling (column D) elective requirement for the major. Prerequisite: MATH 224 and prerequisite or corequisite MATH 212. Offered every other fall.

Partial differential equations (PDEs) arise naturally to model spatially-dependent systems that occur in physics, biology, and finance. This course covers analytic, numerical and qualitative methods for the solution and understanding of PDEs. Topics may include separation of variables, transform methods, nonlinear waves and shocks, and computational methods. This counts toward the computation/modeling/applied (column D) elective requirement for the mathematics major. Prerequisite: MATH 333. Offered every other spring.