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Find similar grantsMathematical Foundations of Artificial Intelligence (MFAI) is sponsored by NSF. Supports research collaborations to establish innovative and principled design and analysis approaches for AI technology using mathematical and statistical frameworks.
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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: Academic institutions and research organizations. Applicants should confirm final requirements in the official notice before submission.
Current published award information indicates Up to $1,500,000 total over three years 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.
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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 ADVANCE program is sponsored by National Science Foundation (NSF). The NSF ADVANCE program aims to broaden the implementation of evidence-based systemic change strategies that promote equity for STEM faculty in academic workplaces and the academic profession. The program provides grants to enhance systemic factors that support equity and inclusion and to mitigate systemic factors that create inequities in the academic profession and workplaces.
EPSCoR Research Infrastructure Improvement Program: Focused EPSCoR Collaborations Program (FEC) is sponsored by U.S. National Science Foundation. The FEC program builds interjurisdictional collaborative teams of EPSCoR investigators in STEM focus areas. Projects are investigator-driven and must include researchers from at least two EPSCoR eligible jurisdictions with complementary expertise to address challenges. The program aims to drive discovery and build sustainable STEM capacity. Tennessee is an EPSCoR-eligible jurisdiction.
Engineering of Biomedical Systems (EBMS) Program is sponsored by U.S. National Science Foundation (NSF). The EBMS program supports fundamental and transformative research at the interface of engineering and biomedical sciences to solve biomedical problems. Projects should focus on high-impact, transformative methods and technologies, including the development of validated models (living or computational) of normal and pathological tissues and organ systems, and advanced biomanufacturing of three-dimensional tissues and organs.
NSF's TechAccess: AI-Ready America program (NSF 26-508) opens with a Round 1 Letter of Intent due June 16 and a budget that scales to $224 million across up to 56 awards — one State or Territory Coordination Hub per state, DC, and U.S. territory. Each hub is $1M/year for three years with a possible fourth, and is tasked with five concrete functions including a public AI resource inventory, a state AI readiness plan, deployment assistance, workforce coordination, and sector convening. The first round funds 10 hubs, the second 20, and the third the remainder — a structure that makes early submission decisively more valuable than late submission. Strategy for state agencies, university systems, EDAs, and nonprofit consortia considering a bid.
Read articleNSF raised its RAPID grant ceiling to $300,000 and EAGER to $400,000 alongside the December 2025 merit review overhaul. With external review now reduced to a two-reviewer minimum and panel discussions optional, the program-officer-driven RAPID and EAGER mechanisms have become more attractive than they have been in two decades. Why investigators with stalled or terminated standard proposals should be writing one-page RAPID concepts this month, and what the new authority structure means for the relationship between PIs and program officers.
Read articleNSF's relaunched SBIR/STTR program under solicitation 26-510 commits $250 million for deep-tech startups, opens Project Pitches June 2, 2026, and sets the first full-proposal deadline for July 27. The Strategic Breakthrough Awards tier — up to $30M per company — is the largest single-company commitment in NSF SBIR history.
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