DEDALE is an interdisciplinary project that intends to develop the next generation of data analysis methods for the new era of big data in astrophysics and compressed sensing. Novel data analysis methods in machine learning allow for a better preservation of the intrinsic physical properties of real data that generally live on intricate spaces, such as signal manifolds.

Our project have three main scientific directions:

i) Introduce new models and methods to analyse and restore complex, multivariate, manifold-based signals.

ii) Exploit the current knowledge in optimisation and operations research to build efficient numerical data processing algorithms in the large-scale settings.

iii) Show the reliability of the proposed methods in two different applications: one in cosmology and one in remote sensing.

DEDALE Highlights Part 1: FORTH