Lattices in Topological Data Analysis (Spring 2026)
Lattices in Topological Data Analysis (Spring 2026)
Topological Data Analysis (TDA) is a relatively new method for understanding the shape of data (a finite collection of points in R^n). This project will consider a novel approach of using TDA's persistence landscapes to analyze datasets that vary in time. Persistence landscapes are functions that "vectorize" the dataset to be used in machine learning pipelines. It turns out that landscapes form a boolean lattice structure defined through the maximum and minimum operations. In this project we will investigate how to leverage this lattice structure to analyze time-varying datasets.
For more information contact Enrique Alvarado (enrique3@iastate.edu)
People:
People:
Enrique Alvarado (Postdoc)
Pre-requisites:
Pre-requisites:
Experience coding in Python or a willingness to learn.