#61: Topological Noise Differentiation — Presentation Detail — Celebration of Scholars

Instructions

Student presentations must have a faculty sponsor.

Abstracts must include a title and a description of the research, scholarship, or creative work. The description should be 150-225 words in length and constructed in a format or style appropriate for the presenter’s discipline.

The following points should be addressed within the selected format or style for the abstract:

A clear statement of the problem or question you pursued, or the scholarly goal or creative theme achieved in your work.
A brief comment about the significance or uniqueness of the work.
A clear description of the methods used to achieve the purpose or goals for the work.
A statement of the conclusions, results, outcomes, or recommendations, or if the work is still in progress, the results you expect to report at the event.

Presenter photographs should be head and shoulder shots comparable to passport photos.

Additional Information

More information is available at carthage.edu/celebration-scholars/. The following are members of the Research, Scholarship, and Creativity Committee who are eager to listen to ideas and answer questions:

Jun Wang
Kim Instenes
John Kirk
Nora Nickels
Andrew Pustina
James Ripley

#61: Topological Noise Differentiation

Name: Hector Rauda
Major: Physics and Mathematics
Hometown: El Salvador
Faculty Sponsor:
Other Sponsors:
Type of research: SURE
Funding: SURE

Abstract

Topological data analysis (TDA) is a field that uses math and computers to understand data structure. It is used in many areas such as science, medicine, and business to find patterns and make predictions. TDA helps us see meaningful connections and structures in data that are not easy to see otherwise.

This study examines how noise affects different data features that change over time. It aims to understand the relationship between noisy data and "topological noise" which is temporary patterns in the data that go away when parameters change. Typically, we ignore topological noise because we consider it irrelevant. However, recent research suggests that specific short-lived patterns in chaotic systems could be significant. The goal is to develop ways to differentiate noise that is meaningful for the data's structure from random noise that does not mean anything.

Poster file