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The goal of the school is to give an overview of signal, image  and data processing and the mathematics that has been developed to tackle some of the problems in these areas.  The topics in mathematics that have evolved from its interaction with signals processing are numerous, and many well-known mathematicians have contributed to its development  including, Norbert Wiener, Lennart Carleson, Claude Shannon, Yves Meyer, Ingrid Daubechies, and Arne Beurling to name a few. The tools that have evolved from the interaction between mathematics and signal processing include the wavelet transform, frame theory, compressed sensing,  and Gabor analysis. Many well-established areas of mathematics have also contributed and evolved from this interaction including, Fourier analysis, splines and approximation theory, functional analysis to name a few. The recent advances in Artificial Intelligence and Deep Learning, and Transport Signal Processing are ripe for the development of new mathematical tools and theory in support of these areas. Learning about the underlying problems in data science and the mathematical tools associated with them is very beneficial to students and researchers in both mathematics and  the disciplines associated with signal and data processing.