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.
Undergraduate students, graduate students and postdocs are invited to participate in this summer school to be held in Quebec City. In particular, women and members of underrepresented groups are especially encouraged to apply for financial support.
Subject to availability of funds, the organizers plan to cover the local accommodation during the summer school. Applicants need to submit:
- Statement of interest (maximum one page)
- One letter of support, e.g., from their director
Please send these to email@example.com.
The deadline for application is March 15th, 2020.
Please indicate in your email if you would like to be considered for financial support.