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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.
Funding Opportunity Number: 24-569. Assistance Listing: 47.041,47.049,47.070,47.075. Funding Instrument: G. Category: ST. Award Amount: $500K – $1.5M per award.
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Mathematical Foundations of Artificial Intelligence (MFAI) | NSF - U.S. National Science Foundation Mathematical Foundations of Artificial Intelligence (MFAI) Important information for proposers and award recipients All proposals must be submitted in accordance with the requirements specified in the funding opportunity and in the Proposal & Award Policies & Procedures Guide (PAPPG) and its supplements .
All NSF grants and cooperative agreements are subject to the applicable set of NSF award terms and conditions . NSF has updated its research security policies for NSF funded projects. Supports research collaborations between mathematicians, statisticians, computer scientists, engineers and social behavior scientists to establish innovative and principled design and analysis approaches for AI technology.
Supports research collaborations between mathematicians, statisticians, computer scientists, engineers and social behavior scientists to establish innovative and principled design and analysis approaches for AI technology.
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 the factors determining the capabilities and limitations of current and emerging generation s 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; e ncouragement of new collaborations in this interdisciplinary research community and between institution s.
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.
October 3, 2024 - Mathematical Foundations of Artificial Intelligence Office… September 19, 2024 - Mathematical Foundations of Artificial Intelligence Office… June 12, 2024 - Mathematical Foundations of Artificial Intelligence Webinar Awards made through this program Browse projects funded by this program Map of recent awards made through this program Directorate for Mathematical and Physical Sciences (MPS) Division of Mathematical Sciences (MPS/DMS) Directorate for Computer and Information Science and Engineering (CISE) Division of Computing and Communication Foundations (CISE/CCF) Division of Information and Intelligent Systems (CISE/IIS) Directorate for Engineering (ENG) Division of Civil, Mechanical and Manufacturing Innovation (ENG/CMMI) Division of Electrical, Communications and Cyber Systems (ENG/ECCS) Directorate for Social, Behavioral and Economic Sciences (SBE) Division of Social and Economic Sciences (SBE/SES)
According to the current listing, eligibility includes: Eligible applicants: 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. institu. Confirm the full requirements in the official notice before applying.
The current listing shows $500K – $1.5M per award. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Applications for Mathematical Foundations of Artificial Intelligence are due October 9, 2026. Build your timeline backwards from this date to cover registrations, approvals, and final submission checks.
Yes — Mathematical Foundations of Artificial Intelligence is offered by U.S. National Science Foundation and this listing comes from Grants.gov, an official U.S. federal source. Federal applications generally require registrations (for example SAM.gov or an agency submission portal), so allow extra lead time.
Yes — this listing is flagged as national in scope, so applicants across the U.S. may apply, subject to the sponsor's other eligibility criteria.
Applications go through the funder's official portal — the Apply Now link on this page goes there directly.
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