CBMS Conference: Elastic Functional and Shape Data Analysis (EFSDA)

这个国家科学基金会奖为CBMS会议提供支持：弹性功能和形状数据分析（EFSDA），将于2018年7月16日至20日在哥伦布的俄亥俄州州立大学举行。主要讲师是佛罗里达州立大学统计系的Anuj Srivastava教授。会议将采用弹性方法的功能和形状数据的统计分析系列讲座，使用黎曼几何，希尔伯特空间方法和计算科学的工具。本次会议的主要重点将是几何方法，特别是使用具有所需不变性的弹性黎曼度量，以及已被证明可以简化计算的形状的平方根表示。这些方法使联合登记和统计分析的功能数据，并被称为弹性的原因。统计目标包括功能和形状数据对象的比较、汇总、聚类、建模和测试。提出的功能和形状数据的统计分析工具在几乎所有的科学分支都有广泛的应用。在这一前沿研究领域促进教育、培训和合作将对社区产生巨大影响。本次研讨会的观众将包括来自统计，应用数学，工程，计算机科学和生物科学的早期职业研究人员。通过在一个重要的STEM领域培训和教育研究人员，这一努力将促进参与者之间未来的跨学科合作。近年来，黎曼几何在统计数据分析，特别是形状分析中的应用取得了巨大的进步。EFSDA汇集了来自不同学科的工具，如几何，统计，功能数据分析，计算科学和应用领域，以开发广泛而全面的解决方案。一方面，它提出了基本的数学问题，包括最佳功能匹配的存在性和唯一性，另一方面，它提供了有效的计算实现的问题处理对齐，降维，功能和形状数据的统计建模。鉴于所有科学学科的功能数据激增，这些工具将有助于满足重要和紧迫的数据分析需求。课堂式的讲座将通过多个讨论环节得到加强。有关会议网页，请参阅https://stat.osu.edu/cbms-efsda。

This National Science Foundation award provides support for the CBMS Conference: Elastic Functional and Shape Data Analysis (EFSDA), which will be held in July 16-20, 2018 at The Ohio State University in Columbus, OH. The primary lecturer is Professor Anuj Srivastava from the Department of Statistics at Florida State University. The conference will feature a lecture series on elastic methods for statistical analysis of functional and shape data, using tools from Riemannian geometry, Hilbert space methods, and computational science. The main focus of this conference will be on geometric approaches, especially on using elastic Riemannian metrics with desired invariance properties, and square-root representations of shape that have proven to simplify computations. These approaches enable joint registration and statistical analysis of functional data, and are termed elastic for that reason. The statistical goals include comparisons, summarization, clustering, modeling, and testing of functional and shape data objects. The proposed tools for statistical analysis of functional and shape data have broad applications in almost all branches of science. Any promotion of education, training, and collaboration in this cutting-edge research area will have a strong impact on the community. The audience for this workshop will include early career researchers from statistics, applied mathematics, engineering, computer science and biological sciences. By training and educating researchers in an important STEM area, this effort will facilitate future interdisciplinary collaborations amongst participants. Recent years have seen a tremendous advancement in the use of Riemannian geometry in statistical data analysis, especially in shape analysis. EFSDA brings together tools from diverse disciplines, such as geometry, statistics, functional data analysis, computational science, and application domains, to develop a broad and comprehensive package of solutions. On one hand, it poses fundamental mathematical questions, including existence and uniqueness of optimal functional matching, and on the other, it provides efficient computational implementations for problems dealing with alignment, dimension reduction, and statistical modeling of functional and shape data. Given the proliferation of functional data in all scientific disciplines, these tools will help address important and urgent data analysis needs. The classroom-style lectures will be enhanced by multiple discussion sessions. For the conference webpage, please see https://stat.osu.edu/cbms-efsda.