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Research in Data Science

  • Ellen Gasparovic
  • Carlotta Domeniconi

Part of the Association for Women in Mathematics Series book series (AWMS, volume 17)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Natalie Durgin, Rachel Grotheer, Chenxi Huang, Shuang Li, Anna Ma, Deanna Needell et al.
    Pages 1-14
  3. Priya Mani, Marilyn Vazquez, Jessica Ruth Metcalf-Burton, Carlotta Domeniconi, Hillary Fairbanks, Gülce Bal et al.
    Pages 15-45
  4. F. Patricia Medina, Linda Ness, Melanie Weber, Karamatou Yacoubou Djima
    Pages 47-80
  5. Elizabeth Munch, Anastasios Stefanou
    Pages 109-127
  6. Asli Genctav, Murat Genctav, Sibel Tari
    Pages 167-175
  7. Franziska Seeger, Anna Little, Yang Chen, Tina Woolf, Haiyan Cheng, Julie C. Mitchell
    Pages 177-197
  8. Robert Aroutiounian, Kathryn Leonard, Rosa Moreno, Robben Teufel
    Pages 199-209
  9. Natalie Durgin, Rachel Grotheer, Chenxi Huang, Shuang Li, Anna Ma, Deanna Needell et al.
    Pages 211-237
  10. Anna Grim, Boris Iskra, Nianqiao Ju, Alona Kryshchenko, F. Patricia Medina, Linda Ness et al.
    Pages 239-281
  11. Venera Adanova, Sibel Tari
    Pages 283-297

About this book

Introduction

This edited volume on data science features a variety of research ranging from theoretical to applied and computational topics. Aiming to establish the important connection between mathematics and data science, this book addresses cutting edge problems in predictive modeling, multi-scale representation and feature selection, statistical and topological learning, and related areas.  Contributions study topics such as the hubness phenomenon in high-dimensional spaces, the use of a heuristic framework for testing the multi-manifold hypothesis for high-dimensional data, the investigation of interdisciplinary approaches to multi-dimensional obstructive sleep apnea patient data, and the inference of a dyadic measure and its simplicial geometry from binary feature data. 
Based on the first Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place in 2017 at the Institute for Compuational and Experimental Research in Mathematics (ICERM) in Providence, Rhode Island, this volume features submissions from several of the working groups as well as contributions from the wider community.  The volume is suitable for researchers in data science in industry and academia. 

Keywords

data science data analysis multiple measurement vectors statistical and topological inference hubness phenomenon multi-manifold hypothesis predictive models data storage data-driven modeling geometry-based classification Association for Women in Mathematics

Editors and affiliations

  • Ellen Gasparovic
    • 1
  • Carlotta Domeniconi
    • 2
  1. 1.Department of MathematicsUnion CollegeSchenectadyUSA
  2. 2.Department of Computer ScienceGeorge Mason UniversityFairfaxUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-11566-1
  • Copyright Information The Author(s) and the Association for Women in Mathematics 2019
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-030-11565-4
  • Online ISBN 978-3-030-11566-1
  • Series Print ISSN 2364-5733
  • Series Online ISSN 2364-5741
  • Buy this book on publisher's site
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