A full schedule of the workshop can be found on the workshop website : http://macsenet.eu/Workshop

This one-day workshop will bring together researchers working in the area of sparse representations and compressed sensing to find out about the latest developments in theory and applications of these approaches, and to explore directions for future research.

The concept of sparse representations deals with systems of linear equations where only a small number of the coefficients are non-zero. The technique of compressed sensing aims to efficiently sense and reconstruct a signal from few measurements, typically by exploiting the sparse structure of the underlying representation. These techniques have proved very popular over the last decade or so, with new theoretical developments, and successful applications in areas such as hyperspectral imaging, brain imaging, audio signal processing and graph signal processing.

This one-day workshop, organized by the SpaRTaN and MacSeNet Initial/Innovative Training Networks*, will include invited keynote talks by Karin Schnass (Universität Innsbruck, Austria) and Jean-Luc Starck (CEA-Saclay, France), oral presentations and posters. The talks and posters will include theoretical advances in sparse representations, dictionary learning and compressed sensing, as well as advances in areas such as brain imaging and MRI, hyperspectral imaging, audio and visual signal processing, inverse imaging problems, and graph-structured signals.

PhD students and Early Career Researchers wishing to bring along a poster of their work for the poster session are encouraged to contact macsenet@surrey.ac.uk with a brief abstract of their work. Posters do not need to be novel: this is an opportunity to showcase work and discuss it with others in the field. There will be an opportunity for discussions to continue after the end of the formal workshop.

Further details can be found on the workshop website : http://macsenet.eu/Workshop

* European Union's Seventh Framework Programme (FP7-PEOPLE-2013-ITN) under grant agreement n° 607290 SpaRTaN and H2020 Framework Programme (H2020-MSCA-ITN-2014) under grant agreement n° 642685 MacSeNet