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Compressive Sensing and Statistical Learning with Sparsity

Research project WASP funded AI/Math project in compressive sensing and statistical learning with sparsity.

WASP has a special program for developing the mathematics of AI and strengthen Sweden's position in the field. This project is a part of the WASP recruitment program and is intended to further develop the research environment within the field mathematics for AI, with focus on compressive sensing and statistical learning with sparsity, at the Department of Mathematics and Mathematical Statistics, Umeå University. The project contains a large recruitment package including one tenure track assistant professorship, two PhD student, and discretionary funds, with a budget about 14 MSEK in 5 years.

Head of project

Jun Yu

Project overview

Project period:

2019-09-01 2024-08-31

Participating departments and units at Umeå University

Department of Mathematics and Mathematical Statistics

Research area

Mathematics, Statistics

External funding

Knut and Alice Wallenberg Foundation

Project description

Human intelligence builds up on what we read, observe, learn, sense and experience. It is our ability to store large amount of data, accumulated in the past and co-relating a few data points to answer a certain question that makes us intelligent. The main aim of artificial intelligence (AI) is to infuse intelligence to agents (such as robots, algorithms trading stocks, autonomous cars) so that it will replace humans in every aspect. AI, in the present, are complex and effective but nowhere near human intelligence. For machines to replicate human intelligence novel methods need to be developed in many elds including statistical learning and compressed sensing.

The project aims to develop new mathematical, statistical and computational methodology for intelligent compressive sensing and statistical learning, establish the theoretical framework, and explore their applications in AI, including medical imaging, automated quality control, and self-driving cars. Understanding and exploiting sparsity to extract useful information from big datasets with purpose of making optimal decisions will be the main focus.

External funding