Geometry and Topology of Data
(December 11 - 13, 2017)
The scale, dimensionality, and complexity of large data has given rise to new topological and geometric methods for understanding what features in a data set are robust under perturbations of the system. Tools from algebraic topology and coarse geometry have been brought fruitfully to bear in a number of contexts leading to a surge of interest in persistent homology, combinatorial geometry, and discrete Morse theory.
Likewise, new frameworks have emerged from harmonic analysis to develop diffusion geometries for large data, enabling multi-scale analyses, and other dynamical approaches to understanding complex data sets. Tools for enabling visualization of each of these methods are in development and increasingly granting researchers the ability to understand their data in new ways.
This workshop will bring together a broad range of researchers for a short workshop to attempt to set directions for future research. This workshop is part of the Brown Data Science Initiative's new NSF TRIPODS grant (dsi.brown.edu), and is run in collaboration with ICERM.
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