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Research in Data Science

  • Book
  • © 2019

Overview

Editors:
  1. Ellen Gasparovic

    1. Department of Mathematics, Union College, Schenectady, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Carlotta Domeniconi

    1. Department of Computer Science, George Mason University, Fairfax, USA

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    You can also search for this editor in PubMed   Google Scholar

  • Highlights the fundamental role of mathematics in the development of data science
  • Features a diverse range of topics from the theoretical to the applied and computational
  • Based on the 2017 Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop

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

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-xiv
    Download chapter PDF

  2. Sparse Randomized Kaczmarz for Support Recovery of Jointly Sparse Corrupted Multiple Measurement Vectors

    • Natalie Durgin, Rachel Grotheer, Chenxi Huang, Shuang Li, Anna Ma, Deanna Needell et al.
    Pages 1-14

  3. The Hubness Phenomenon in High-Dimensional Spaces

    • Priya Mani, Marilyn Vazquez, Jessica Ruth Metcalf-Burton, Carlotta Domeniconi, Hillary Fairbanks, Gülce Bal et al.
    Pages 15-45

  4. Heuristic Framework for Multiscale Testing of the Multi-Manifold Hypothesis

    • F. Patricia Medina, Linda Ness, Melanie Weber, Karamatou Yacoubou Djima
    Pages 47-80

  5. Interdisciplinary Approaches to Automated Obstructive Sleep Apnea Diagnosis Through High-Dimensional Multiple Scaled Data Analysis

    • Giseon Heo, Kathryn Leonard, Xu Wang, Yi Zhou
    Pages 81-107

  6. The ℓ ∞-Cophenetic Metric for Phylogenetic Trees As an Interleaving Distance

    • Elizabeth Munch, Anastasios Stefanou
    Pages 109-127

  7. Inference of a Dyadic Measure and Its Simplicial Geometry from Binary Feature Data and Application to Data Quality

    • Linda Ness
    Pages 129-166

  8. A Non-local Measure for Mesh Saliency via Feature Space Reduction

    • Asli Genctav, Murat Genctav, Sibel Tari
    Pages 167-175

  9. Feature Design for Protein Interface Hotspots Using KFC2 and Rosetta

    • Franziska Seeger, Anna Little, Yang Chen, Tina Woolf, Haiyan Cheng, Julie C. Mitchell
    Pages 177-197

  10. Geometry-Based Classification for Automated Schizophrenia Diagnosis

    • Robert Aroutiounian, Kathryn Leonard, Rosa Moreno, Robben Teufel
    Pages 199-209

  11. Compressed Anomaly Detection with Multiple Mixed Observations

    • Natalie Durgin, Rachel Grotheer, Chenxi Huang, Shuang Li, Anna Ma, Deanna Needell et al.
    Pages 211-237

  12. Analysis of Simulated Crowd Flow Exit Data: Visualization, Panic Detection and Exit Time Convergence, Attribution, and Estimation

    • Anna Grim, Boris Iskra, Nianqiao Ju, Alona Kryshchenko, F. Patricia Medina, Linda Ness et al.
    Pages 239-281

  13. A Data Driven Modeling of Ornaments

    • Venera Adanova, Sibel Tari
    Pages 283-297
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Keywords

About this book

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. 

Editors and Affiliations

  • Department of Mathematics, Union College, Schenectady, USA

    Ellen Gasparovic

  • Department of Computer Science, George Mason University, Fairfax, USA

    Carlotta Domeniconi

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