Project Grant 2550265
- The National Science Foundation Division of Computing and Communication Foundations awarded The Johns Hopkins University $362,815 on August 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to support research on structural and algorithmic foundations of variational inequalities in machine learning. The project develops foundational theory and scalable algorithms for systems with multiple interacting objectives, addressing gaps between existing...
- The National Science Foundation Division of Computing and Communication Foundations awarded The Johns Hopkins University $188,168 on July 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) for collaborative research in large-scale bilevel optimization theory, algorithms, and applications. The award extends through July 31, 2027, with performance at Baltimore, Maryland. The project develops fast and scalable Hessian-free bilevel algorithms with convergence...
- Federal Grant Award Summary The National Science Foundation's Division of Computing and Communication Foundations awarded The Johns Hopkins University $525,000 under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) for a collaborative research project spanning August 15, 2025 through July 31, 2028. This project grant supports fundamental research in min-max optimization theory and algorithm development, addressing critical gaps in mathematical frameworks and...
- 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 Office of Integrative Activities awarded The Johns Hopkins University $1.146 million on August 1, 2026, under the Geosciences program (CFDA 47.050) to accelerate ocean data assimilation using machine learning techniques. The project will develop methods to integrate ocean observations with computer models of ocean currents through machine learning, with the aim of reducing computational cost and improving reliability of data assimilation predictions. The work...
- The National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation awarded Virginia Polytechnic Institute & State University $639,433 on May 15, 2026, through the Engineering CAREER program (CFDA 47.041) to develop machine learning-enhanced branch-and-bound algorithms for scalable global optimization of mixed-integer nonlinear programs. The research creates new optimization algorithms that use machine learning to accelerate solution times while preserving...
- Federal Project Grant Award Summary The National Science Foundation's Engineering program (CFDA 47.041) awarded The Johns Hopkins University a $273,797 Project Grant effective October 1, 2025, through September 30, 2028, to conduct collaborative research on efficient bilevel optimization methods for planning and control applications. The primary deliverables include the development of novel algorithms and computational tools designed to address constrained, nonconvex bilevel optimization...
- Federal Grant Award Summary The National Science Foundation (NSF) Division of Mathematical Sciences awarded The Johns Hopkins University a Project Grant of $209,998 on August 15, 2025, under the Mathematical and Physical Sciences program (CFDA 47.049). This project, scheduled for completion by July 31, 2028, develops mathematical and computational tools to learn the dynamics of complex high-dimensional systems from ensemble data—observational snapshots rather than complete trajectories. The...
- This $700,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support The Johns Hopkins University's research on amorphous metal additive manufacturing. The project aims to develop simulation-informed models and computational tools to enable design of additively manufactured amorphous metals with desired strength and toughness properties. This will involve using machine learning to quantify structural order parameters...
- This Project Grant award of $450,000 from the National Science Foundation's Engineering program (CFDA 47.041) will support research on bi-level optimization for hierarchical machine learning problems. The award to the Regents of the University of Minnesota, conducting the work through their Office of Sponsored Projects Administration, aims to develop new approaches for modeling, analyzing, and innovating on a wide array of emerging machine learning applications using bi-level optimization...
The National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation awarded The Johns Hopkins University $520,028 on October 1, 2026, under the Engineering program (CFDA 47.041) for research developing mathematical foundations that integrate machine learning with discrete optimization algorithms. The research investigates the fundamental interplay between mixed-integer optimization, discrete geometry, and machine learning. The project will develop new mathematical tools to determine data requirements for learning-enhanced optimization algorithms to perform reliably and to characterize the expressive power and computational complexity of neural network models used in optimization. The work will simultaneously apply formal methods from optimization and discrete geometry to answer foundational questions about machine learning itself, creating a reciprocal exchange between the two fields. The resulting algorithms, mathematical frameworks, and theoretical guarantees are intended to advance both optimization and artificial intelligence by establishing a rigorous foundation for future learning-enhanced decision-making systems across manufacturing, transportation, energy, healthcare, and scientific discovery applications. The project will educate graduate and undergraduate students at the intersection of optimization, artificial intelligence, and applied mathematics through research, coursework, and interdisciplinary collaborations. Performance is located in Baltimore, Maryland, with a period of performance from October 1, 2026, through September 30, 2029.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $520.0k | 7/31/26 |