High Dimensional Data Analysis from 2018 – 2021



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.



Original image courtesy of Elizabeth Sawchuk.  Experiment performed by Vegard Antun (University of Oslo)



Ben Adcock, SFU

Roger Donaldson, UBC

Michael Friedlander, UBC

Yaniv Plan, UBC

Ozgur Yilmaz
, UBC 

Aleksandr Aravkin, UWashington 


Planned Activities:


2018 Activities

May 7, 2018: Kick-off retreat at UBC Robson Square

July 30-31, 2018: Optimization for Data Science, a two-day summer school at UBC

August 1-3, 2018: Foundations of Data Science Workshop at UBC

December 8 - 9, 2018: Sparse Recovery, Learning, and Neural Networks, special session at the CMS Winter meeting 


2019 Activities:

March  4-5, 2019: Minicourse on uncertainty quantification of PDEs with random coefficients, at SFU-Burnaby   

July 22-25, 2019: CRG Summer School on Deep learning for Computational Mathematics, at SFU-Burnaby