Project Grant 2603785

Federal Project Grant Summary

The National Science Foundation (NSF), Division of Mathematical Sciences, awarded $200,000 to The Research Foundation For The State University of New York (RF-SUNY) at the University at Albany under the Mathematical and Physical Sciences program (CFDA 47.049) for the period September 1, 2026, through August 31, 2029. This project grant funds research and development of data-driven nonlinear model reduction methods designed to generate compact, accurate computational models of complex, high-dimensional stochastic dynamical systems. The deliverables include new mathematical and computational tools that extract long-term behavioral insights from short simulation data bursts, enabling researchers to identify stable states, estimate transition rates, and predict long-time dynamics with significantly improved computational efficiency compared to existing direct simulation approaches.

The project delivers both research outputs and educational products. Research products include adaptive exploration algorithms, topological data analysis methods, and open-source software tools applicable to molecular and chemical simulation, biological modeling, and other computational science domains. Educational deliverables include curriculum development (a new statistical machine learning course at the University at Albany), open-source educational materials in topological data analysis and data science, training for graduate and undergraduate students, and outreach programs providing interactive artificial intelligence activities to high school students from under-resourced communities.