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.
