### About

The Directed Reading Program (DRP) is a program in which undergraduate students are paired with graduate student mentors for semester-long independent study projects. All interested undergraduates must apply to join the Program.

Mentor/mentee pairings are based on mathematical interests and availability. Each mentee meets weekly with his/her mentor for about an hour. The details of these meetings are left up to the mentee/mentor pairs; they might include presentations by the mentee, informal lecturing by the mentor, general discussion, questions about exercises, etc. In addition to the meetings, the mentee is expected to work independently for at least four hours each week. At the end of the semester, each student gives a 15-20 minute presentation on a topic related to the project. This presentation is to be widely accessible and is meant to be introductory rather than a time to “show off” with highly technical material: it is far better to give a survey of several interesting theorems than one detailed proof.

### Applications

Applications are welcome for the spring and fall semesters via the online DRP Application Form. Applicants will be asked to provide one reference, a list of courses they’ve taken and a short description of the mathematical interests.

Applicants must have completed Math 2710 or Math 2142Q and be self-motivated and capable of sustaining an independent study project for the duration of the semester. If mentors are in short supply, applicants with less background may be asked to re-apply the following semester.

If you have any questions please don’t hesitate to email us.

### Projects

Most DRP projects are based on a particular book or article that the mentee reads at his/her own pace, guided and supplemented by the mentor. Below are slides of some of the presentations.

- Algebraic Combinatorics,
- Algebraic Number Theory
- Measure Theory and Signal Filtering
- Computability (1)
- Computability (2)
- Finite Element Analysis,
- Fuzzy Logic
- Hilbert Spaces
- Lie Groups
- Linear Algebra and Data Learning
- Matrix Groups
- Measure Theory and Fractals
- Metric Spaces
- Nonlinear Dynamics
- Numerical Analysis
- Propositional Logic
- Self-similar Fractals,
- Semi-simple Lie Algebras
- Set Theory
- Stochastic Processes