Advances in data science, published as volume 26 in Springer’s “Association for Women in Mathematics” series, summarizes the results from two related workshops--the first one held at the Institute for Computational and Experimental Research in Mathematics (ICERM), Providence, Rhode Island, in August 2019, and the second one at Trier University in the Robert-Schuman-Haus, Trier, Germany, in July 2018. The topics covered are quite interdisciplinary and related to cutting-edge research in data science.
The book is divided into four parts, with the first three parts containing three chapters each and Part 4 with four chapters, for a total of 13 chapters in all. Part 1, on image processing techniques, deals with image reconstruction, edge sharpening, and barcode image analysis. Part 2, “Shape and Geometry,” describes methods for boundary detection, learning
geometric shape, and feature extraction in light detection and ranging (LiDAR) data. Part 3, “Machine Learning,” looks at interpolation techniques using piece-wise linear neural networks, dynamic topic modeling dealing with the emergence and evolution of topics from multidimensional and temporally evolving datasets, and sparse recovery of time-varying signals. The last part, “Data Analysis,” deals with dynamics in distributed political networks, the classification of human sleep states using topological data analysis and Markov chain theory, statistical learning theory applied to the analysis of sleep apnea data, and estimating blood alcohol concentration (BAC) levels using data from biosensors.
This book describes results from the forefront of research in data science and would greatly benefit aspiring researchers at the master’s and PhD levels. Each chapter contains ample references to the related literature. The appendix lists the participants from the two workshops that led to this volume.