High Dimensional Data Analysis

2018 2021
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High Dimensional Data Analysis: Sampling

There are fundamental open questions that limit the industrial uptake of ideas from the mathematics of high-dimensional data and their application in practice. These include bridging the gap between the sampling required by theory and what is efficient in practice; translating the theory from analogue to digital; developing algorithms that scale gracefully to big data applications; and implementation of open-source software, which serves as an effective means of technology transfer.

The aim of this CRG to address these questions.Three particular focal areas are:

- Bridging the gap between theory and practice in applications of sparse recovery
- Methods for large-scale optimization
- Deep learning and sampling.

Sampling
Original image courtesy of Elizabeth Sawchuk.  Experiment performed by Vegard Antun (University of Oslo)
Professor of Mathematics, Simon Fraser University
Motorola Solutions
IBM Professor of Computer Science and Professor of Mathematics, UBC
University of British Columbia
University of Washington