Mathematical Foundations of Artificial Intelligence
Can you apply?
This grant is for research teams investigating the mathematical foundations of artificial intelligence. Eligible applicants include higher education institutions (two- and four-year colleges, community colleges) and nonprofit research organizations located in the U.S. Research projects must involve interdisciplinary collaboration among mathematicians, statisticians, computer scientists, engineers, and social scientists. Focus areas include understanding AI capabilities and limitations, developing design principles for AI systems, and ensuring reliability of machine learning algorithms.
Program description
Machine Learning and Artificial Intelligence (AI) are enabling extraordinary scientific breakthroughs in fields ranging from protein folding, natural language processing, drug synthesis, and recommender systems to the discovery of novel engineering materials and products. These achievements lie at the confluence of mathematics, statistics, engineering and computer science, yet a clear explanation of the remarkable power and also the limitations of such AI systems has eluded scientists from all disciplines. Critical foundational gaps remain that, if not properly addressed, will soon limit advances in machine learning, curbing progress in artificial intelligence. It appears increasingly unlikely that these critical gaps can be surmounted with increased computational power and experimentation alone. Deeper mathematical understanding is essential to ensuring that AI can be harnessed to meet the future needs of society and enable broad scientific discovery, while forestalling the unintended consequences of a disruptive technology.
The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE) will jointly sponsor research collaborations consisting of mathematicians, statisticians, computer scientists, engineers, and social and behavioral scientists focused on the mathematical and theoretical foundations of AI. Research activities should focus on the most challenging mathematical and theoretical questions aimed at understanding the capabilities, limitations, and emerging properties of AI methods as well as the development of novel, and mathematically grounded, design and analysis principles for the current and next generation of AI approaches.
Specific research goals include: establishing a fundamental mathematical understanding of thefactors determining the capabilities and limitations of current and emerging generations of AI systems, including, but not limited to, foundation models, generative models, deep learning, statistical learning, federated learning, and other evolving paradigms; the development of mathematically grounded design and analysis principles for the current and next generations of AI systems; rigorous approaches for characterizing and validating machine learning algorithms and their predictions; research enabling provably reliable, translational, general-purpose AI systems and algorithms; encouragement of new collaborations in this interdisciplinary research community and between institutions.
The overall goal is to establish innovative and principled design and analysis approaches for AI technology using creative yet theoretically grounded mathematical and statistical frameworks, yielding explainable and interpretable models that can enable sustainable, socially responsible, and trustworthy AI.
Who can apply
Eligible applicants
How to apply
Application links
Key dates & requirements
Required documents
- NSF proposal cover sheet (NSFFORM 1207)
- Project narrative/research proposal
- Budget and budget justification
- Biographical sketches of PI and key personnel
- Facilities and equipment description
- Bibliography of cited references
- Data management plan
Program contact
- 👤 U.S. National Science Foundation
- 📧 grantsgovsupport@nsf.gov
- 📞 703-292-4203
Funding track record
Recent awards under CFDA 47.070 from the last 3 years — real organizations that won funding through this same program.
Top 10 Largest Recent Awards
-
$975,888,088
-
$376,000,000
-
$146,395,788
-
$84,249,997
-
$78,999,134
-
$38,082,925
-
$37,758,328
-
$37,023,406
-
$36,793,220
-
$31,497,099
Top States by Funding
- CO 6 awards $1,049.0M
- TX 9 awards $651.6M
- IL 10 awards $304.8M
- CA 16 awards $228.2M
- IN 3 awards $93.7M
Source: USAspending.gov — federal spending transparency. Data covers last 3 years.
Funding history
Annual funding for this program — Federal obligations (CFDA 47.070). How funding has trended year over year.
| 2024 | $965,230,000 | |
| 2025 | $916,340,000 | |
| 2026 est. | $331,630,000 |
FAQ
What types of organizations can apply?
Accredited institutions of higher education and nonprofit research organizations located in the U.S. can submit proposals. Community colleges are eligible.
Does this grant require matching funds or cost-sharing?
No cost-sharing is required for this grant.
What research activities does this grant support?
Projects focusing on mathematical understanding of AI systems, design principles for machine learning, algorithm validation, and provably reliable AI systems.
How competitive is this grant?
This is a highly competitive NSF research grant with significant funding pool and broad national scope. Strong theoretical contributions and team qualifications are essential.
What is the typical award range?
Individual awards typically range from $500,000 to $1,500,000 depending on project scope and team composition.
💡 Tips for applicants
- Assemble a genuinely interdisciplinary team. Mathematics and computer science alone are insufficient; include statisticians and domain experts.
- Ground your proposal in fundamental theoretical questions, not applications. NSF prioritizes foundational research over engineering solutions.
- Clearly articulate the mathematical gaps your work addresses. Explain why current computational approaches cannot solve these problems alone.
- Demonstrate how your research will advance understanding of AI capabilities and limitations across multiple paradigms (deep learning, generative models, etc.).
- Use the collaborative funding structure strategically. Design your project scope and team to justify the proposed budget range.
⚠️ Common mistakes
Focusing excessively on applications rather than fundamental mathematical theory. Proposing single-discipline projects instead of true interdisciplinary collaboration. Failing to articulate clear mathematical research questions distinct from engineering challenges.
Similar grants
- OPEN FY26 Bureau of Land Management Rangeland Resource Management – Bureau wide — Bureau of Land Management
- OPEN FY26 Bureau of Land Management Cultural and Paleontological Resource Management – Bureau wide — Bureau of Land Management
- OPEN FY26 Bureau of Land Management Youth Conservation Corps – Bureau wide — Bureau of Land Management
- OPEN Infertility Training Center — Office of the Assistant Secretary for Health
- OPEN Integrated Research, Education, and Extension Competitive Grants Program – Organic Transitions — National Institute of Food and Agriculture