Computational Topology for Data Analysis

Tamal Krishna Dey

ISBN 10: 1009098160 / ISBN 13: 9781009098168

Editore: Cambridge University Press, 2022

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Druck auf Anfrage Neuware - Printed after ordering - This book provides a computational and algorithmic foundation for techniques in topological data analysis, with examples and exercises.

Codice articolo 9781009098168

Dati bibliografici

• Titolo: Computational Topology for Data Analysis
• Editore: Cambridge University Press
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 1009098160
• ISBN 13: 9781009098168
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 235 L x 157 A x 29 P (millimetri) 235 millimetri di larghezza per 157 millimetri di altezza per 29 millimetri di profondità
• Peso: 803 grammi

Descrizione articolo

Riassunto

This book provides a computational and algorithmic foundation for techniques in topological data analysis, with examples and exercises.

Informazioni sugli autori

Tamal Krishna Dey is Professor of Computer Science at Purdue University. Before joining Purdue, he was a faculty in the CSE department of The Ohio State University. He has held academic positions at Indiana University-Purdue University at Indianapolis, Indian Institute of Technology Kharagpur, and Max Planck Institute. His research interests include computational geometry, computational topology and their applications to geometric modeling and data analysis. He has (co)authored two books Curve and Surface Reconstruction: Algorithms with Mathematical Analysis (Cambridge University Press) and Delaunay Mesh Generation (CRC Press), and (co)authored more than 200 scientific articles. Dey is a fellow of the IEEE, ACM, and Solid Modeling Association.

Yusu Wang is Professor in the Halıcıouğlu Data Science Institute at University of California, San Diego. Prior to joining UCSD, she was Professor of Computer Science and Engineering at the Ohio State University and post-doctoral fellow at Stanford University. Yusu primarily works in topological and geometric data analysis, developing effective and theoretically justified algorithms for data analysis using geometric and topological ideas, as well as in applying them to practical domains. She received the DOE Early Career Principal Investigator Award in 2006 and NSF Career Award in 2008.