This National Science Foundation (NSF) Project Grant, under the CFDA program titled "Mathematical and Physical Sciences" (CFDA 47.049), aims to advance machine learning techniques by developing new mathematical tools to better analyze and visualize complex, high-dimensional data. The $126,025 award to the University of Utah will fund research focused on tackling key challenges in manifold learning algorithms, including handling noise, preserving geometric details, and effectively clustering collections of manifolds. The project will investigate best practices for denoising data, analyze how noise impacts spectral manifold learning algorithms, and develop novel algorithms that utilize diffusion to faithfully encode geometric information at various scales. Additionally, the project will contribute a robust and scalable solution for clustering collections of manifolds via a novel angle-based path metric on simplices. This comprehensive data analysis pipeline aims to enable more reliable and scalable tools for data analysis while promoting inclusion and innovation in science and education. The award period runs from June 1, 2025, to May 31, 2030.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $126.0k | 1/21/25 |