The National Science Foundation (NSF) has opened applications for the Mathematical Foundations of Artificial Intelligence grant program. With an estimated total funding of $8.5 million, the initiative aims to support research collaborations focused on the mathematical and theoretical underpinnings of AI. Spanning various disciplines including mathematics, computer science, engineering, and social sciences, the grants seek to address critical gaps in understanding AI systems’ capabilities and limitations. Eligible applicants include non-profit organizations, research laboratories, and accredited institutions of higher education in the U.S. The closing date for applications is October 10, 2024. For further information, contact NSF grants.gov support.
Opportunity ID: 353936
General Information
Document Type:Funding Opportunity Number:Funding Opportunity Title:Opportunity Category:Opportunity Category Explanation:Funding Instrument Type:Category of Funding Activity:Category Explanation:Expected Number of Awards:CFDA Number(s):Cost Sharing or Matching Requirement:
| Grants Notice |
| 24-569 |
| Mathematical Foundations of Artificial Intelligence |
| Discretionary |
| Grant |
| Science and Technology and other Research and Development |
| 47.041 — Engineering 47.049 — Mathematical and Physical Sciences 47.070 — Computer and Information Science and Engineering 47.075 — Social, Behavioral, and Economic Sciences |
| No |
Version:Posted Date:Last Updated Date:Original Closing Date for Applications:Current Closing Date for Applications:Archive Date:Estimated Total Program Funding:Award Ceiling:Award Floor:
| Synopsis 1 |
| May 02, 2024 |
| May 02, 2024 |
| Oct 10, 2024 |
| Oct 10, 2024 |
| Nov 08, 2026 |
| $ 8,500,000 |
| $1,500,000 |
| $500,000 |
Eligibility
Eligible Applicants:Additional Information on Eligibility:
| Others (see text field entitled “Additional Information on Eligibility” for clarification) |
| *Who May Submit Proposals: Proposals may only be submitted by the following: -Non-profit, non-academic organizations: Independent museums, observatories, research laboratories, professional societies and similar organizations located in the U.S. that are directly associated with educational or research activities. – <span>Institutions of Higher Education (IHEs) – Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members.</span> *Who May Serve as PI: <div class=”OutlineElement Ltr SCXW177155816 BCX0″> <p class=”Paragraph SCXW177155816 BCX0″><span class=”TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0″><span class=”TextRun SCXW177155816 BCX0″ lang=”EN-US” xml:lang=”EN-US” data-contrast=”none”><span class=”NormalTextRun SCXW177155816 BCX0″>As of the date the proposal is </span><span class=”NormalTextRun SCXW177155816 BCX0″>submitted</span><span class=”NormalTextRun SCXW177155816 BCX0″>, any PI, co-PI, or senior/key personnel must hold either:</span></span></span><span class=”EOP TrackedChange SCXW177155816 BCX0″ data-ccp-props=”{“></span> <ul> <li><span class=”TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0″><span class=”TextRun SCXW177155816 BCX0″ lang=”EN-US” xml:lang=”EN-US” data-contrast=”none”>a tenured or tenure-track position, </span></span><span class=”TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0″><span class=”TextRun SCXW177155816 BCX0″ lang=”EN-US” xml:lang=”EN-US” data-contrast=”none”>or</span></span><span class=”EOP TrackedChange SCXW177155816 BCX0″ data-ccp-props=”{“></span></li> <li><span class=”TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0″><span class=”TextRun SCXW177155816 BCX0″ lang=”EN-US” xml:lang=”EN-US” data-contrast=”none”>a primary, full-time, paid appointment in a research or teaching position</span></span></li> </ul> <span class=”TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0″><span class=”TextRun SCXW177155816 BCX0″ lang=”EN-US” xml:lang=”EN-US” data-contrast=”none”><span class=”NormalTextRun SCXW177155816 BCX0″>at a US-based campus of an organization eligible to </span><span class=”NormalTextRun SCXW177155816 BCX0″>submit</span><span class=”NormalTextRun SCXW177155816 BCX0″> to this solicitation (see above), with exceptions granted for family or medical leave, as </span><span class=”NormalTextRun SCXW177155816 BCX0″>determined</span><span class=”NormalTextRun SCXW177155816 BCX0″> by the </span><span class=”NormalTextRun SCXW177155816 BCX0″>submitting</span><span class=”NormalTextRun SCXW177155816 BCX0″> organization. Individuals with </span></span></span><span class=”TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0″><span class=”TextRun SCXW177155816 BCX0″ lang=”EN-US” xml:lang=”EN-US” data-contrast=”none”>primary</span></span><span class=”TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0″><span class=”TextRun SCXW177155816 BCX0″ lang=”EN-US” xml:lang=”EN-US” data-contrast=”none”> appointments at for-profit non-academic organizations or at overseas branch campuses of U.S. institutions of higher education are not eligible.</span></span><span class=”EOP TrackedChange SCXW177155816 BCX0″ data-ccp-props=”{“></span></div> |
Additional Information
Agency Name:Description:Link to Additional Information:Grantor Contact Information:
| National Science Foundation |
| 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 across this interdisciplinary research community and from diverse 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. |
| NSF Publication 24-569 |
| If you have difficulty accessing the full announcement electronically, please contact: NSF grants.gov support |