Reconstructing linearly embedded graphs: A first step to stratified space learning

<p style='text-indent:20px;'>In this paper, we consider the simplest class of stratified spaces – linearly embedded graphs. We present an algorithm that learns the abstract structure of an embedded graph and models the specific embedding from a point cloud sampled from it. We use tools and inspiration from computational geometry, algebraic topology, and topological data analysis and prove the correctness of the identified abstract structure under assumptions on the embedding. The algorithm is implemented in the Julia package Skyler, which we used for the numerical simulations in this paper.</p>

Liberty Needs Glasses

This research investigates the outcomes of school communities who leverage an antiracist political agenda to address the racial achievement gap. In New York City, the Equity and Excellence for All agenda is a series of programs and mandates designed to improve student achievement through an antiracist, equity driven lens. This chapter examines policy implementation with an understanding of preexisting school culture conditions that impact policy efficacy. Toward this goal, this research is part of a larger body of work that prioritizes data from the vantage point of students, teachers, and school leaders. A multi-methods study, this research leverages a priori quantitative analysis to articulate distinction in school race culture and then qualitative analysis to capture the counter narrative of the marginalized who are situated in the educationally stratified spaces.