Tuition Fee
Not Available
Start Date
2026-09-01
Medium of studying
Not Available
Duration
Not Available
Details
Program Details
Degree
Bachelors
Major
Applied Mathematics | Mathematics | Statistics
Area of study
Mathematics and Statistics
Course Language
English
Intakes
| Program start date | Application deadline |
| 2025-09-01 | - |
| 2025-12-01 | - |
| 2026-09-01 | - |
| 2026-12-01 | - |
| 2027-09-01 | - |
| 2027-12-01 | - |
About Program
Program Overview
Advanced Research Project
The Advanced Research Project, denoted as MATH/STAT*4600, offers undergraduate students the opportunity to engage in mathematics and statistics research projects under the supervision of faculty members each semester. This project provides a unique chance for students to work on advanced topics that align with their interests and academic goals.
Program Structure
- The project is available as a 1.00 credit course, offered in both the Fall and Winter semesters.
- Students can register for either MATH4600 or STAT4600, depending on their research focus.
Research Opportunities
- Faculty members are available for supervision, with specific research areas including:
- Mathematics of two-dimensional turbulence (Dr. Geordie Richards)
- Exploring Variable and Model Selection Techniques for Multinomial Response Data (Dr. Nagham Mohammad)
- Spatial-temporal models, Bayesian statistics, time series, and applications to epidemiology (Dr. Justin Slater)
- Additional research opportunities may be available by contacting faculty members not listed, to inquire about potential projects.
Semester-Specific Research Projects
Fall 2025
- MATH*4600: Mathematics of two-dimensional turbulence, supervised by Dr. Geordie Richards.
Winter 2026
- MATH*4600:
- Mathematics of two-dimensional turbulence, supervised by Dr. Geordie Richards.
- A project supervised by Dr. Matthew Demers, with details available upon request.
- STAT*4600:
- Exploring Variable and Model Selection Techniques for Multinomial Response Data, supervised by Dr. Nagham Mohammad.
- Spatial-temporal models, Bayesian statistics, time series, and applications to epidemiology, supervised by Dr. Justin Slater.
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