Editors:
Reports cutting-edge methodologies in data science
Involves various types of data, offering strong potential for idea exchange and new applications
Features highly interdisciplinary research problems, promoting cross-field collaboration
Part of the book series: Association for Women in Mathematics Series (AWMS, volume 26)
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Table of contents (13 chapters)
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Front Matter
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Data Analysis
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Front Matter
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Back Matter
About this book
This volume highlights recent advances in data science, including image processing and enhancement on large data, shape analysis and geometry processing in 2D/3D, exploration and understanding of neural networks, and extensions to atypical data types such as social and biological signals. The contributions are based on discussions from two workshops under Association for Women in Mathematics (AWM), namely the second Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place between July 29 and August 2, 2019 at the Institute for Computational and Experimental Research in Mathematics (ICERM) in Providence, Rhode Island, and the third Women in Shape (WiSh) Research Collaboration Workshop that took place between July 16 and 20, 2018 at Trier University in Robert-Schuman-Haus, Trier, Germany.
These submissions, seeded by working groups at the conference, form a valuable source for readers who are interested in ideas and methods developed in interdisciplinary research fields. The book features ideas, methods, and tools developed through a broad range of domains, ranging from theoretical analysis on graph neural networks to applications in health science. It also presents original results tackling real-world problems that often involve complex data analysis on large multi-modal data sources.Keywords
- data analysis
- spatial-temporal dynamics modeling
- incomplete and multi-modal data
- health science
- networking
- user anonymity
- statistical topological learning algorithms
- discrete approximations
- methods involving duality
- regularization
Reviews
Editors and Affiliations
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Intel (United States), Hermosa Beach, USA
Ilke Demir
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School of Natural Sciences & Mathematics, The University of Texas at Dallas, Richardson, USA
Yifei Lou
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Department of Mathematics, Wilfrid Laurier University, Waterloo, Canada
Xu Wang
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Fakultät für Maschinenbau, Helmut Schmidt University, Hamburg, Germany
Kathrin Welker
Bibliographic Information
Book Title: Advances in Data Science
Editors: Ilke Demir, Yifei Lou, Xu Wang, Kathrin Welker
Series Title: Association for Women in Mathematics Series
DOI: https://doi.org/10.1007/978-3-030-79891-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Authors and the Association for Women in Mathematics 2021
Hardcover ISBN: 978-3-030-79890-1Published: 30 November 2021
Softcover ISBN: 978-3-030-79893-2Published: 30 November 2022
eBook ISBN: 978-3-030-79891-8Published: 03 December 2021
Series ISSN: 2364-5733
Series E-ISSN: 2364-5741
Edition Number: 1
Number of Pages: XX, 364
Number of Illustrations: 19 b/w illustrations, 166 illustrations in colour
Topics: Calculus of Variations and Optimization, Probability Theory, Numerical Analysis, Mathematical Applications in Computer Science, Computer Vision, Probability and Statistics in Computer Science