# High Dimensional Data Analysis

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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.

Professor of Mathematics, Simon Fraser University

Motorola Solutions

IBM Professor of Computer Science and Professor of Mathematics, UBC

University of British Columbia

Director

University of Washington