Ben Adcock | PIMS - Pacific Institute for the Mathematical Sciences

Professor of Mathematics, Simon Fraser University

Ben Adcock is a professor of mathematics at Simon Fraser University. His research areas include

Mathematics of data science
Numerical analysis
Machine learning
Applied and computational harmonic analysis
Approximation theory
Deep learning
Compressed sensing
Sampling theory
Spectral methods for PDEs

He received the CAIMS/PIMS Early Career Award in 2017 and is one of the leaders of the PIMS CRG in High Dimensional Data Analysis.

This annual event showcases the computational expertise of our Department and of other invited speakers. The program includes invited talks and a Poster Session which will cover diverse topics in mathematics with an emphasis on computation. All...

Sharpness is a generic assumption in continuous optimization that bounds the distance to the set of minimizers in terms of the suboptimality in the objective function. It leads to the acceleration of first-order optimization methods via so-called...

A video of this event is available on mathtube.org. Many problems in science and engineering require the reconstruction of an object - an image or signal, for example - from a collection of measurements. Due to time, cost or other constraints, one is...

While spectral methods for the numerical solution of PDEs with smooth solutions offer the great advantage of high accuracy, they are typically poorly suited for solving problems with nonsmooth or sharply peaked solutions. This is in great part due to...

Abstract: Compressed sensing has been one of the great successes of applied mathematics in the last decade. It allows one to reconstruct sparse signals from seemingly incomplete collections of measurements, and thereby circumvent the classical...

Compressed sensing concerns the recovery of signals and images from seemingly incomplete data sets. Introduced nearly a decade ago, it has since become an intensive area of research in applied mathematics, engineering and computer science. However...

Many problems in scientific computing require the approximation of smooth, high-dimensional functions from limited amounts of data. For instance, a typical problem in uncertainty quantification involves identifying the parameter dependence of the...

Deep learning lies at the forefront of the artificial intelligence revolution. Stunning successes has been achieved by deep learning for challenging tasks such as image classification. Yet, current deep learning implementations have a tendency to be...