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Advances in Data Science

  • Book
  • © 2021


  • 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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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.

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Table of contents (13 chapters)

  1. Image Processing

  2. Shape and Geometry

  3. Machine Learning

  4. Data Analysis


“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)

Editors and Affiliations

  • Intel (United States), Hermosa Beach, USA

    Ilke Demir

  • School of Natural Sciences & Mathematics, The University of Texas at Dallas, Richardson, USA

    Yifei Lou

  • Department of Mathematics, Wilfrid Laurier University, Waterloo, Canada

    Xu Wang

  • Fakultät für Maschinenbau, Helmut Schmidt University, Hamburg, Germany

    Kathrin Welker

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