Research in Data Science

1. Front Matter

   Pages i-xiv

   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

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. 

• 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

• Department of Mathematics, Union College, Schenectady, USA

  Ellen Gasparovic

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

  Carlotta Domeniconi

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