The interaction between Harmonic Analysis, Functional Analysis, Approximation Theory, Signal and Image Processing, Machine Learning, Big Data, and other areas of engineering produced fundamental tools that benefited the scientific community and were powerful for applications, e.g., Wavelet Theory, B-Splines, Compressed Sensing, and many others.
Data Science is an all-encompassing field that uses many tools from Statistics, Computer Science, and Mathematics. In particular, recently deep learning techniques solved problems that were considered intractable. Although the new techniques in Data Science are extremely powerful, the mathematical underpinning is still in its infancy and needs to be better developed.
The goal of this conference is to bring together experts from various areas of Mathematics and Data Science in order to learn about the state of the art tools and advances in Data Science, the current open problems in the area, the mathematical challenges, and the new mathematical problems that state of the art methods produced. Ultimately, this interaction will help new developments in Mathematics and Data Science and will produce new tools and new understanding that can be used to solve problems more efficiently and with better theoretical guarantees.