1,000+ Opportunities
Find the right grant
Search federal, foundation, and corporate grants with AI — or browse by agency, topic, and state.
Mathematical Foundations of Artificial Intelligence (MFAI) is sponsored by National Science Foundation (NSF) Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE).
This program supports research collaborations focused on the mathematical and theoretical foundations of AI and machine learning, particularly in areas like recommender systems. The goal is to establish a fundamental mathematical understanding of the capabilities and limitations of current and emerging AI systems, and to develop mathematically grounded design and analysis principles.
Get alerted about grants like this
Get emailed when new opportunities from “National Science Foundation (NSF) Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE)” or related funders appear. Free, weekly, unsubscribe anytime.
Or search similar grants →Extracted from the official opportunity page/RFP to help you evaluate fit faster.
Mathematical Foundations of Artificial Intelligence (MFAI) | NSF - U.S. National Science Foundation Mathematical Foundations of Artificial Intelligence (MFAI) NSF's implementation of the revised 2 CFR NSF Financial Assistance awards (grants and cooperative agreements) made on or after October 1, 2024, will be subject to the applicable set of award conditions, dated October 1, 2024, available on the NSF website .
These terms and conditions are consistent with the revised guidance specified in the OMB Guidance for Federal Financial Assistance published in the Federal Register on April 22, 2024.
Important information for proposers All proposals must be submitted in accordance with the requirements specified in this funding opportunity and in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is being submitted. It is the responsibility of the proposer to ensure that the proposal meets these requirements.
Submitting a proposal prior to a specified deadline does not negate this requirement.
Updates to NSF Research Security Policies On July 10, 2025, NSF issued an Important Notice providing updates to the agency's research security policies, including a research security training requirement, Malign Foreign Talent Recruitment Program annual certification requirement, prohibition on Confucius institutes and an updated FFDR reporting and submission timeline.
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: Collaborations consisting of mathematicians, statisticians, computer scientists, engineers, and social and behavioral scientists. Confirm the full requirements in the official notice before applying.
The current listing shows expected to range from $500,000 to $1,500,000 total for a duration of 36 months. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Mathematical Foundations of Artificial Intelligence (MFAI) is funded by National Science Foundation (NSF) Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE). Verify program details on the funder's official page before applying.
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.
Past winners and funding trends for this program
Mathematical Foundations of Artificial Intelligence is sponsored by National Science Foundation (NSF) Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE). This grant supports interdisciplinary research on mathematical and theoretical foundations for sustainable, trustworthy, and socially responsible AI. It addresses foundational gaps in AI through collaborations between mathematicians, statisticians, computer scientists, engineers, and social and behavioral scientists. The goal is to establish innovative and principled design and analysis approaches for AI technology, yielding explainable and interpretable models for sustainable, socially responsible, and trustworthy AI.
Mathematics Foundations of Artificial Intelligence (MFAI) is sponsored by National Science Foundation (NSF) Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE). Mathematics Foundations of Artificial Intelligence (MFAI) is sponsored by National Science Foundation (NSF) Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic …
Mathematical Foundations of Artificial Intelligence is sponsored by U.S. National Science Foundation (NSF) Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE). This program sponsors research collaborations focused on the mathematical and theoretical foundations of AI. It addresses challenging mathematical and theoretical questions to understand AI's capabilities, limitations, and emerging properties.
Economics of AI Fellowship is sponsored by Stripe. The fellowship supports foundational academic research in the economics of AI, an area currently understudied despite rapid technical progress in artificial intelligence. Fellows receive a baseline grant, opportunities to attend conferences with leading economists and technologists, and potential access to unique data via Stripe and its customers.
The UKRI Policy Fellowships 2025, funded by the Economic and Social Research Council, offer 18-month placements for academics to co-design research with UK government and What Works Network host organizations. Awards range from £180,000 to £280,000 and support three fellowship tracks: core policy fellows, Natural Hazards and Resilience policy fellows, and What Works Innovation fellows. Applicants must hold a PhD or equivalent research experience, be based at a UKRI-eligible UK organization, and possess relevant subject matter or methodological expertise. Government-hosted positions target early to mid-career academics, while What Works fellowships welcome all career stages. Fellows work directly with policymakers to bridge academic research and policy development on pressing national and global challenges. The application deadline is July 15, 2025.
The FY2027 budget proposes eliminating NSF's Social, Behavioral, and Economic Sciences directorate entirely. With only 613 grants funded this year, social scientists face an existential funding crisis.
Read articleNSF's Arctic Research Opportunities solicitation funds roughly 75 awards a year — up to $50 million — across six program areas from natural sciences to social sciences to the Arctic Observing Network. The July 15, 2026 target date is not a hard deadline, and understanding that distinction is the first strategic decision an Arctic researcher makes. Here is how the six doors differ and how to choose the right one.
Read articleNSF 26-514, 26-515 and 26-518 put $310 million and up to 850 awards on rolling submission with zero per-PI limits, while chemistry rations two proposals a year and materials rations one. The Engineering directorate ran this experiment eight years ago. When NSF tried it in Earth Sciences, proposal volume fell 59 percent. Here is what that history predicts, and how to use a directorate that is not counting your submissions.
Read article