Learning Dynamic Worlds: Advances in Functional and Spatio-Temporal Data Science
Conference
Proposal Description
Recent advances in data collection have produced increasingly complex data with inherent spatial, temporal, and functional structures. This session focuses on new methodologies and applications at the intersection of functional data analysis, spatio-temporal statistics, and data science. Emphasis will be placed on innovative modeling frameworks, computational strategies, and learning paradigms for analyzing dynamic, high-dimensional data arising in several scientific domains.