Mathematics Achievement Fund

Our world is at a pivotal moment where the boundaries dividing the physical and social worlds from the cyber world have become blurred. Cyberspace has evolved from an interconnected digital environment into a complex and interdependent cyber ecosystem that involves hardware, software, networks, data, people, organizations, countries, and the physical world. Critical functions of everyday life are deeply intertwined with computing, including health, government, commerce, the public sphere, education, critical infrastructure, interpersonal communication, and transportation. The complexity and inter-dependencies in cyberspace can be misused and exploited by malicious actors. These in turn can trigger adverse outcomes such as disruption of critical infrastructure and systems; theft of intellectual property and sensitive data; amplification of inequalities; disclosure of private information of individuals, organizations, and governments; and threats to lives, livelihoods, and reputations. Furthermore, constant attacks on the data and assets of corporations, governments, and individuals undermine people’s trust in decision-making and processes that depend critically on these cyber systems. The Security, Privacy, and Trust in Cyberspace (SaTC 2.0) program aims to build trust in global cyber ecosystems. Trust is the core tenet of this program and, for the purposes of this solicitation, is broadly defined to include our confidence in the security, privacy, and resilience of cyberspace, particularly in the face of malicious intent. Achieving this level of confidence in cyberspace requires not only understanding the vulnerabilities in a system that could be exploited and how they can be addressed, but also understanding the social and technical dimensions of trust in cyber systems, along with the educational efforts needed to increase public awareness of risks in cyberspace, and building a well-trained corps of privacy and security professionals. SaTC 2.0 spans the interests of NSF's Directorates for Computer and Information Science and Engineering (CISE), Mathematical and Physical Sciences (MPS), Social, Behavioral and Economic Sciences (SBE), and STEM Education (EDU). Proposals must be submitted pursuant to one of the following designations, each of which may have additional requirements: RES: The Research (RES) designation is the focus of the multidisciplinary SaTC 2.0 research program. RES projects are limited to $1,200,000 in total budget, with durations of up to four years. Proposals with a total budget of more than $600,000 have additional requirements including Broadening Participation in Computing and collaboration plans. RES proposals may include an optional Transition to Education (TTE) plan with a budget up to $50,000 (within the RES total budget request) to co-evolve novel educational initiatives in the context of the proposed research. EDU: The Education (EDU) designation is used to identify proposals focusing on education and workforce training in building trust in security, privacy, and resilience of cyberspace. EDU proposals are limited to $500,000 in total budget, with durations of up to three years. EDU proposals that primarily focus on education research with demonstrated collaboration, as reflected in the PI team between cybersecurity subject matter experts and education researcher(s), may request an additional $100,000 beyond the $500,000 limit. SEED: The Seedling (SEED) category is intended for special topics defined by accompanying Dear Colleague Letters. SEED projects are limited to $300,000 in total budget, with durations of up to two years. Funding Opportunity Number: 25-515. Assistance Listing: 47.049,47.070,47.075,47.076. Funding Instrument: G. Category: ST. Award Amount: $50K – $1.2M per award.

Mathematical Foundations of Artificial Intelligence (MFAI) is sponsored by National Science Foundation (NSF). 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.     Mathematical Foundations of Artificial Intelligence (MFAI). Program guideline: NSF 24-569. Upcoming due date listed in NSF feed.