- Algebraic and Arithmetic Geometry
- Applied Combinatorics
- Applied Mathematics
- Computer Algebra
- Discrete Mathematics
- History of Mathematics
- Industrial Mathematics
- Mathematics, Genomics & Prediction in Infection & Evolution - MAGPIE
- Mathematics and Data
- Mathematics of Communications
- Number Theory
- Operations Research
- Centre for Operations Research and Decision Sciences
- News & Events
Jeff Wiens, MSc (’11), PhD (’14), Research Analyst at MDA
With two graduate degrees in mathematics at SFU, Wiens enjoyed the freedom to pursue the problems he was most interested in.
Jeffrey Wiens studied applied mathematics at SFU, completing an MSc and a PhD. His Master’s research focused on vehicle flow traffic where he analytically solved the Riemann problem for a hyperbolic conservation law with a discontinuous flux function which was used to construct a high resolution finite volume scheme. During his Doctorate, he developed an efficient algorithm for approximating solutions to fluid-structure interaction problems on distributed-memory computer clusters using the immersed boundary method. “The immersed boundary method is a mathematical and numerical framework that allows researchers to simulate the interaction between a fluid and a deformable solid. For example, the immersed boundary method has been used at the Courant Institute to simulate the blood flow in a beating human heart, which capture the two-way interaction between the blood (fluid) and muscle structure of the heart (deformable solid). Simulating large fluid-structure interaction problems are computationally expensive and generally need to solved using multiple computers,” he explains.
For Wiens, a highlight of his graduate studies was the freedom to decide what to study. “Being able to focus on problems you’re interested in allowed me to drive my own path,” he says. “SFU is a place where you can learn for the sake of learning.”
After graduation, Wiens began working for MDA on the RADARSAT Constellation Mission as a Software Engineer. “MDA builds a lot of systems for the Canadian government including the Canadian Space Agency and the Department of National Defence,” says Wiens. “Recently, I moved into the R&D department at MDA as a research analyst where I am solving geospatial analytics problems using deep learning.” Wiens credits his mathematics background for giving him the means to tackle a variety of problems across different domains.