Hypothesis Testing Involving Candidate Topological Spaces To Learn How Topological Noise Arises Through Different Probability Distributions

Session Number

Project ID: MATH 04

Advisor(s)

Ryan Robinett; University of Chicago

Discipline

Mathematics

Start Date

19-4-2023 9:05 AM

End Date

19-4-2023 9:20 AM

Abstract

Topological data analysis (TDA) combines algebraic topology and other tools from pure mathematics to allow for a rigorous study of the 'shape' inherent to data. The foundational tool is persistent homology, an extension of homology to point cloud data. Persistent homology has been applied to various types of data in computer vision, manufacturing of porous materials, and cell differentiation trajectories.

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Apr 19th, 9:05 AM Apr 19th, 9:20 AM

Hypothesis Testing Involving Candidate Topological Spaces To Learn How Topological Noise Arises Through Different Probability Distributions

Topological data analysis (TDA) combines algebraic topology and other tools from pure mathematics to allow for a rigorous study of the 'shape' inherent to data. The foundational tool is persistent homology, an extension of homology to point cloud data. Persistent homology has been applied to various types of data in computer vision, manufacturing of porous materials, and cell differentiation trajectories.