IPI special issue on 'mathematical/statistical approaches in data science' in the Inverse Problem and Imaging

Abstract

Data science is an interdisciplinary field about extracting knowledge or insights from data. It involves computational and applied mathematics, statistics, computer science, engineering, and domain sciences. In an effort to bring together researchers from different disciplines to report on cutting-edge methodologies in data science, Dr. Yifei Lou at the University of Texas at Dallas (UTD), together with Drs. Weihong Guo (Case Western Reserve University), Jing Qin (University of Kentucky), and Ming Yan (Michigan State University), organized a workshop, entitled “Recent Developments on Mathematical/Statistical Approaches in Data Science,” held at the UTD’s campus, on June 1-June 2 2019. To better disseminate the results, this special issue in the journal of Inverse Problems and Imaging (IPI) assembles peer reviewed articles from some of the invited speakers. The scope of the special issue is centered at data science, aiming to collect state-of-the-art computational algorithms and novel applications in data processing. The topics range from compressive sensing, machine learning, image processing, variational and PDE-based models, large-scale optimization, and data-driven applications.