Project Grant 2617221
- The National Science Foundation Division of Mathematical Sciences awarded the University of Southern California $250,000 on September 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop machine learning methods for geometry and topology. The project establishes a systematic framework for applying modern machine learning to open problems in symplectic geometry and low-dimensional topology. Work will organize around three complementary approaches: training...
- The National Science Foundation Division of Mathematical Sciences awarded The Leland Stanford Junior University $333,333 on September 1, 2026, for collaborative research developing machine learning methods for geometry and topology under the Mathematical and Physical Sciences program (CFDA 47.049). The project establishes a systematic framework for applying modern machine learning to open problems in symplectic geometry and low-dimensional topology. Work centers on three complementary modes:...
- The National Science Foundation Division of Mathematical Sciences awarded New York University $145,000 on August 15, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to support research on random planar maps and their scaling limits. The project will establish scaling limit results for random planar maps and study the limiting random surfaces known as Liouville quantum gravity surfaces. The research encompasses related topics including Schramm-Loewner evolutions (random...
- The National Science Foundation Division of Mathematical Sciences awarded Northeastern University $300,000 on July 1, 2026, for a Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to develop machine learning methodologies for stratified matrix manifolds under group actions. The project will integrate topology, geometry, and machine learning to advance data-driven discovery across scientific domains, organized into three thrusts: (1) mathematical and statistical...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $400,000 Project Grant to New York University (NYU) for the period of August 1, 2023 to July 31, 2026. The grant, funded under the Mathematical and Physical Sciences program (CFDA 47.049), focuses on two key areas: Studying geometric evolution equations, specifically mean curvature flow and Ricci flow, with a focus on different aspects of regularity. This research aims to address longstanding open problems at the...
- The National Science Foundation Division of Mathematical Sciences awarded $200,000 to The Research Foundation For The State University Of New York on September 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop data-driven nonlinear model reduction methods for high-dimensional, multiscale stochastic dynamical systems. The project will create mathematical and computational tools that learn compact, accurate models of long-term behavior from short bursts of...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $107,860 Project Grant to the Regents of the University of Minnesota, Office of Sponsored Projects Administration, a non-profit 1862 land grant college, to conduct research under the NSF Mathematical and Physical Sciences program (CFDA 47.049). The research project will develop theoretical foundations for using machine learning methods to solve high-dimensional partial differential equations, emphasizing predictive...
- The National Science Foundation Division of Mathematical Sciences awarded the University of Pennsylvania $350,000 on June 15, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) for collaborative research in geometric scientific machine learning for partial differential equations with tensorial constraints. The project develops physics-preserving machine learning models that embed geometric and physical constraints directly into AI architectures. These structure-preserving...
- The National Science Foundation Division of Mathematical Sciences awarded New York University $163,189 on January 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop connections between analytic number theory and other areas of mathematics, specifically dynamics and probability theory. The research explores how deterministic processes randomize when sampled at regularly spaced intervals (the integers), and applies techniques from probability and mathematical...
- The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $331,902 Project Grant to the Trustees of Boston University on August 15, 2023 under the Mathematical and Physical Sciences program (CFDA 47.049). The purpose of this 3-year grant is to develop rigorous mathematical analysis and theory for the training algorithms used in neural network models across various machine learning applications. The research will leverage stochastic analysis and weak convergence theory...
The National Science Foundation Directorate for Mathematical and Physical Sciences awarded New York University $416,000 on September 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop machine learning methods for geometry and topology. The project establishes a systematic framework for applying modern machine learning to open problems in symplectic geometry and low-dimensional topology. Work centers on three complementary modes: training problem-specific neural networks and reinforcement-learning agents to search for geometric constructions; refining and sampling near-optimal constructions using diffusion-based generative models; and distilling machine-discovered strategies into short, interpretable algorithms that mathematicians can verify, generalize, and extend. The symplectic ball-packing problem—determining how efficiently balls can be embedded into a given region while preserving signed areas of surfaces—serves as a central testbed, alongside questions about Lagrangian submanifolds, the topology of four-dimensional spaces, and related structures. Machine-generated constructions will be converted into rigorous mathematics through certification processes. The project supports graduate students and postdoctoral researchers and will release openly available software and datasets to lower barriers for other researchers applying machine learning in mathematics. Performance of work takes place in New York, New York, with a period of performance from September 1, 2026, through August 31, 2029. The assistance type is a Project Grant.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $416.0k | 8/12/26 |