Scientific Computing, Machine Learning and Analysis of PDE

Our Research

SFU has award-winning research faculty whose interests lie in the interplay between scientific computing, approximation theory, machine learning and analysis of partial differential equations (PDE). Some of today’s richest mathematical questions and most impactful algorithms arise in this vibrant field.

One of the largest such groups in Canada, our mathematics research includes: analysis of PDE and integral equations, PDEs on surfaces, spectral analysis, foundations of data science, mathematics of machine learning, numerical analysis, mathematical modelling, compressed sensing and sparse recovery. The models and applications we work on range from weather and climate, plasma physics, muscle mechanics, image and signal processing, graphics, optimal design and self-collective behaviour.

This is a fast-moving field, where advanced ideas from computing, machine learning and analysis enhance and inform our understanding of some of the gnarliest continuum models out there.

We haven’t met a challenging continuum model we’re afraid of, yet. 



Ben Adcock

Numerical Analysis, Applied and Computational Harmonic Analysis, Compressed Sensing

Razvan Fetecau

Applied PDE Analysis, Self-Organizing Behaviour

David Muraki

Geophysical Fluid Dynamics, Asymptotic Methods

Nilima Nigam

Numerical Analysis, Spectral Methods, Mathematical Biology

Steve Ruuth

Scientific Computing, PDE's On Surfaces

John Stockie

Computational Fluid Dynamics, Industrial Mathematics

Weiran Sun

Applied PDE Analysis, Kinetic Theory

Manfred Trummer

Scientific Computing, Medical Imaging

Postdoctoral Fellows and Visitors


Graduate Students


If you are a current SFU Mathematics Postdoctoral Fellow or Graduate Student in the Scientific Computing, Machine Learning and Analysis of PDE Research Group, and your name is missing from the above lists, please send an email to Casey Bell.