Federal Grant Award Summary

Cornell University received a $300,000 Project Grant from the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) effective September 1, 2026 through August 31, 2031. This CAREER award funds the development of mathematical and computational methods for solving inverse problems—mathematical problems that infer hidden causes from indirect, incomplete, or noisy observations. The research establishes a transport-based, measure-theoretic framework by formulating inverse problem components over probability measure spaces and investigates three connected research directions: (1) dynamical system models for unstable, ill-posed inverse problems with sparse, noisy data; (2) stochastic inverse problem approaches emphasizing uncertainty quantification beyond standard Bayesian formulations; and (3) inverse operator learning methods. The work targets applications in geoscience, medical imaging, and cancer treatment planning, with emphasis on accelerating radiation therapy planning and improving scientific computing reliability with limited data.

The award incorporates education and workforce development activities including student mentoring, curriculum development, public engagement, and data science workshops designed to broaden participation in STEM fields, with specialized outreach to incarcerated learners and reentry participants. The research advances presidential priorities in artificial intelligence by developing learning-based methods that are reliable, interpretable, and mathematically grounded. Research will be conducted at Cornell University's Ithaca, New York campus.