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About the short course:
As modern data applications become complex in size and structure, identifying the underlying shape and structure has become of fundamental importance. The classical approaches such as dimension reduction are challenging for handling these applications. Topological data analysis (TDA) is a rapidly developing collection of methods that focuses on the “shape” of data. TDA can uncover the underlying low-dimensional geometric and topological structures from high-dimensional datasets. TDA has been successfully applied to various areas, including biology, network data, material science, and geology, in recent years. The goal of the lecture is to introduce novel TDA methods that can capture geometric or topological information of data and make statistical inferences. This lecture aims to familiarize these new methods along with their applications to various types of data.
About the presenter:
Chul Moon received his Ph.D. in Statistics from the University of Georgia. He joined the Department of Statistical Science at Southern Methodist University as an Assistant Professor in 2018. His research interests include topological data analysis, empirical likelihood, and ranked set sampling. His research aims to develop statistical methods in biosciences and geosciences.
Basic statistical knowledge and R
The Early Bird Deadline ends on 26 June 2021.
Source of figure: Ghrist, Robert. "Barcodes: the persistent topology of data." Bulletin of the American Mathematical Society 45.1 (2008): 61-75.
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Marie-Louise Rankin, Executive Officer
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