Federal Project Grant Award Summary

Northeastern University received a $300,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences (CFDA 47.049) awarded July 1, 2026, with completion scheduled for June 30, 2029. This collaborative research initiative will develop novel machine learning methodologies operating on stratified matrix manifolds under group actions, integrating topology, geometry, and machine learning to advance data-enabled discovery. The project is organized into three primary deliverables: (1) establishing mathematical and statistical foundations for stratified matrix manifolds, including noise-robust dimensionality reduction and vector bundle approximation techniques; (2) designing equivariant machine learning algorithms for dimensionality reduction, optimal transport, and flow-matching that preserve manifold structure across strata; and (3) demonstrating application of these methods to computational neuroscience problems, such as detecting neural stimulus space reconfiguration, and quantum science applications involving symmetry-aware dimensionality reduction for Kohn-Sham density functional theory.

Beyond research deliverables, the project includes substantial workforce development and educational components. Northeastern will co-develop cross-institutional graduate seminars on machine learning and establish research experiences for undergraduates, training students in data-driven computation. These educational initiatives and open science efforts are designed to amplify the intellectual contributions of the research while advancing national artificial intelligence competitiveness through enhanced human capital development in machine learning and scientific computing.