Project Grant 2603463

Federal Project Grant Summary

Michigan State University received a $200,000 Project Grant from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049), effective July 1, 2026, through June 30, 2029. This collaborative research award funds the development of an efficient and robust algorithmic framework for large-scale tensor data analysis. The project delivers scalable mathematical and computational tools designed to extract reliable information from large, high-dimensional, noisy, and partially corrupted multidimensional datasets generated by artificial intelligence and modern scientific applications. The research focuses on advancing mode-wise sampling methods, low-rank tensor representations, randomized numerical techniques, and non-convex optimization algorithms with rigorous theoretical foundations, including convergence analysis, perturbation bounds, and sampling reliability guarantees.

The award supports applied research with direct relevance to national priorities in artificial intelligence and advanced computing, with potential applications in medical imaging and national security. In addition to research deliverables, the project advances workforce development through graduate student training (two graduate students) and undergraduate research opportunities in computational mathematics, optimization, and data science. The award includes educational outreach activities designed to promote STEM participation and foster pathways to research and technical careers. The research is performed at Michigan State University's East Lansing, Michigan facility.