Advances in Data Science | ISBN 9783030798932

Advances in Data Science

herausgegeben von Ilke Demir, Yifei Lou, Xu Wang und Kathrin Welker
Mitwirkende
Herausgegeben vonIlke Demir
Herausgegeben vonYifei Lou
Herausgegeben vonXu Wang
Herausgegeben vonKathrin Welker
Buchcover Advances in Data Science  | EAN 9783030798932 | ISBN 3-030-79893-3 | ISBN 978-3-030-79893-2
“The topics covered are quite interdisciplinary and related to cutting-edge research in data science. … 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.” (S. Lakshmivarahan, Computing Reviews, February 21, 2023)

Advances in Data Science

herausgegeben von Ilke Demir, Yifei Lou, Xu Wang und Kathrin Welker
Mitwirkende
Herausgegeben vonIlke Demir
Herausgegeben vonYifei Lou
Herausgegeben vonXu Wang
Herausgegeben vonKathrin Welker

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.