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.





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