Opportunities

Overview

I am always looking for talented undergraduate, graduate and postdoctoral researchers to join my group.  I take on students with degrees in mathematics, computer science, engineering or related disciplines. The main requirement is a strong background in mathematics.

If you are a prospective student or postdoc please take a look at my Research page to get an overview of the type of research my group does, as well as the list of recent projects below.

You are welcome to email me to discuss your application. However, due to the volume of emails I receive, I am unable to respond to all inquiries. Make sure your email clearly explains (i) why you are interested in working with me, and (ii) your relevant experience.

Opportunities

Here is a list of opportunities broken down by category:

Postdocs

None at this time.  Please check back later.

Graduate Students

Prospective MSc and PhD students who wish to work in my group need to apply to the SFU Mathematics graduate program.  The application deadline is typically in January of each year.

Undergraduate Students

MITACS Globalink: I normally advertise several projects in the MITACS Globalink Research Internship program.  This program offers funding for 12-week summer research projects for senior undergraduate students.  Please check out http://mitacs.ca/en/programs/globalink for details.

SFU USRA/VPR: I typically host one or more undergraduate projects as part of the USRA/VPR program (for students from both SFU and elsewhere).  These are normally advertised in December of each year on the SFU Mathematics website.

Recent and Ongoing Student Projects

Practical Deep Learning for Scientific Computing

Practical Approximation via Neural Networks and Deep Learning

High-dimensional Approximation in Irregular Domains

Optimal Algorithms for Compressive Imaging

Deep Learning Techniques for Inverse Problems in Imaging

Frame Approximation with Bounded Coefficients

Weighted l1 minimization techniques for compressed sensing and their applications

Compressive Imaging with Total Variation Regularization and Application to Auto-calibration of Parallel Magnetic Resonance Imaging