IPI special issue on 'mathematical/statistical approaches in data science' in the Inverse Problem and Imaging

IF 1.2 4区数学 Q2 MATHEMATICS, APPLIED

Inverse Problems and Imaging Pub Date : 2021-01-01 DOI:10.3934/ipi.2021007

Weihong Guo, Y. Lou, Jing Qin, Ming Yan

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引用次数: 0

Abstract

Data science is an interdisciplinary field about extracting knowledge or insights from data. It involves computational and applied mathematics, statistics, computer science, engineering, and domain sciences. In an effort to bring together researchers from different disciplines to report on cutting-edge methodologies in data science, Dr. Yifei Lou at the University of Texas at Dallas (UTD), together with Drs. Weihong Guo (Case Western Reserve University), Jing Qin (University of Kentucky), and Ming Yan (Michigan State University), organized a workshop, entitled “Recent Developments on Mathematical/Statistical Approaches in Data Science,” held at the UTD’s campus, on June 1-June 2 2019. To better disseminate the results, this special issue in the journal of Inverse Problems and Imaging (IPI) assembles peer reviewed articles from some of the invited speakers. The scope of the special issue is centered at data science, aiming to collect state-of-the-art computational algorithms and novel applications in data processing. The topics range from compressive sensing, machine learning, image processing, variational and PDE-based models, large-scale optimization, and data-driven applications.

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关于“数据科学中的数学/统计方法”的IPI特刊在逆问题和成像中

数据科学是一个从数据中提取知识或见解的跨学科领域。它涉及计算和应用数学、统计学、计算机科学、工程学和领域科学。为了将不同学科的研究人员聚集在一起，报告数据科学的前沿方法，德克萨斯大学达拉斯分校(University of Texas at Dallas, UTD)的楼亦菲博士(Yifei Lou)和dr。郭卫红(凯斯西储大学)，秦靖(肯塔基大学)和闫明(密歇根州立大学)，组织了一个研讨会，题为“在数据科学数学/统计方法的最新发展，”在UTD的校园举行，于2019年6月1日至6月2日。为了更好地传播这些结果，《逆问题与成像》(IPI)杂志的这一期特刊汇集了一些受邀演讲者的同行评议文章。本期特刊的范围以数据科学为中心，旨在收集最新的计算算法和数据处理中的新应用。主题包括压缩感知、机器学习、图像处理、基于变分和pde的模型、大规模优化和数据驱动的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Inverse Problems and Imaging 数学-物理：数学物理

CiteScore

2.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Inverse Problems and Imaging publishes research articles of the highest quality that employ innovative mathematical and modeling techniques to study inverse and imaging problems arising in engineering and other sciences. Every published paper has a strong mathematical orientation employing methods from such areas as control theory, discrete mathematics, differential geometry, harmonic analysis, functional analysis, integral geometry, mathematical physics, numerical analysis, optimization, partial differential equations, and stochastic and statistical methods. The field of applications includes medical and other imaging, nondestructive testing, geophysical prospection and remote sensing as well as image analysis and image processing. This journal is committed to recording important new results in its field and will maintain the highest standards of innovation and quality. To be published in this journal, a paper must be correct, novel, nontrivial and of interest to a substantial number of researchers and readers.