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Full proposal deadline October 9, 2026 at 5pm local time. No LOI or preliminary proposal required.
NSF Mathematical Foundations of Artificial Intelligence (MFAI) Program is sponsored by National Science Foundation (NSF). The NSF Mathematical Foundations of Artificial Intelligence (MFAI) Program is a grant from the National Science Foundation (NSF) that funds research collaborations between mathematicians, statisticians, computer scientists, engineers, and social and behavioral scientists to deve…
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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)
Based on current listing details, eligibility includes: Universities and research institutions. PI or co-PI can be part of no more than one proposal per deadline. Collaborative interdisciplinary teams required across multiple disciplines. Applicants should confirm final requirements in the official notice before submission.
Current published award information indicates $500,000 - $1,500,000 Always verify allowable costs, matching requirements, and funding caps directly in the sponsor documentation.
The current target date is October 9, 2026. Build your timeline backwards from this date to cover registrations, approvals, attachments, and final submission checks.
Federal grant success rates typically range from 10-30%, varying by agency and program. Build a strong proposal with clear objectives, measurable outcomes, and a well-justified budget to improve your chances.
Requirements vary by sponsor, but typically include a project narrative, budget justification, organizational capability statement, and key personnel CVs. Check the official notice for the complete list of required attachments.
Yes — AI tools like Granted can help research funders, draft proposal sections, and check compliance. However, always review and customize AI-generated content to reflect your organization's unique strengths and the specific requirements of the solicitation.
Review timelines vary by funder. Federal agencies typically take 3-6 months from submission to award notification. Foundation grants may be faster, often 1-3 months. Check the program's timeline in the official solicitation for specific dates.
Many federal programs offer multi-year funding or allow competitive renewals. Check the official solicitation for continuation and renewal policies. Non-competing continuation applications are common for multi-year awards.
Improving Undergraduate STEM Education: Education & Human Resources (IUSE: EHR) Program is sponsored by National Science Foundation (NSF). This program promotes novel, creative, and transformative approaches to generating and using new knowledge about STEM teaching and learning to improve STEM education for undergraduate students. It supports projects that bring recent advances in STEM knowledge into undergraduate education, adapt, improve, and incorporate evidence-based practices, and lay the groundwork for institutional improvement in STEM education. Professional development for instructors to ensure adoption of new and effective pedagogical techniques is a potential topic of interest.
Agricultural Technologies (AG) - NSF SBIR/STTR is sponsored by National Science Foundation (NSF). The Agricultural Technologies topic supports innovations enabling farm production ecosystems that support the proper utilization of natural resources. Such technologies may encompass systems-level and multidisciplinary solutions to enable complex agricultural practices that support increased biodiversity balanced with yield production. Sub-topics include food waste mitigation, resilient supply & distribution, and other agricultural technologies.
NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs is sponsored by National Science Foundation (NSF). These programs provide non-dilutive funds for use-inspired research and development of unproven, leading-edge technology innovations that address societal challenges. NSF funds broadly across scientific and engineering disciplines and does not solicit specific technologies.