05/31/2024 Edition 129
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Computing in Undergraduate Education |
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The Improving Undergraduate STEM Education: Computing in Undergraduate Education (IUSE: CUE) program aims to better prepare a wider, more diverse range of students to collaboratively use computation across a range of contexts and challenging problems. With this solicitation, the National Science Foundation focuses on re-envisioning how to teach computing effectively to a broad group of students, in a scalable manner, with an emphasis on broadening participation of groups who are underrepresented and underserved by traditional computing courses and careers.
Proposals will be funded across three tracks that focus on evidence-based transformative efforts to modernize computing courses and accelerate student success in the knowledge, skills, and dispositions of current and emerging industries, and/or explore effective pathways to computing degrees and careers that involve two-year colleges and industry partnerships.
- The Transformation track focuses on addressing one or more key challenges in transforming undergraduate computing education through innovative programs.
- The Pathways track considers the multiple entry and exit points through two-year colleges as part of effective pathways to computing degrees and/or careers.
- The Mobilizing track aims to develop a shared national vision around innovation and inclusion in undergraduate computing education.
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Mathematical Foundations of AI |
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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.
Specific research goals include: establishing a fundamental mathematical understanding of the factors 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 across this interdisciplinary research community and from diverse institutions.
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Accelerating Computer-Enabled Scientific Discovery |
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The ACED program seeks to harness computing to accelerate scientific discovery, while driving new computing advancements. The intent is to catalyze advancements on both sides of a virtuous cycle that: (a) benefit scientific disciplines through computational technologies and (b) foster novel computing technologies that will enable advances beyond the specific use cases or domains originally targeted. The program seeks continuous collaborations between at least two groups of researchers. One group is expected to consist of researchers in computing, which, for the purposes of this solicitation are those disciplines that are supported by the Core Programs of National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) directorate. The other group of researchers are expected to represent another scientific or engineering discipline, which, for the purposes of this solicitation, are defined as those supported within existing programs of the following NSF directorates: Biological Sciences, Engineering, or Mathematical and Physical Sciences.
The ACED program solicits proposals in two tracks:
- Track I: Emerging Ideas Proposals
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Track II: Discovery Proposals
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Sustainable Polymers Enabled by Emerging Data Analytics |
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The Molecular Foundations for Sustainability: Sustainable Polymers Enabled by Emerging Data Analytics program (MFS-SPEED) is a cross-directorate funding call in response to The National Defense Authorization Act (NDAA) for Fiscal Year (FY) 2021 and the 2022 CHIPS and Science Act. It is supported by the NSF Directorates for Mathematical and Physical Sciences (MPS) and Technology, Innovation, and Partnerships (TIP), and five industry partners: Procter & Gamble, PepsiCo, BASF, Dow, and IBM.
The goal of MFS-SPEED is to support fundamental research enabling the accelerated discovery and ultimate manufacturing of sustainable polymers using state-of-the-art data science, and to enhance development of a cross-disciplinary workforce skilled in this area. In particular, through this solicitation the research community is encouraged to address the discovery and elaboration of new sustainable polymers or sustainable pathways to existing polymers by the creation and use of a data-centric environment where research projects are:
- focused on new approaches to predicting structure and properties of polymers and advanced soft materials,
- with insights enabled by data analytics including Artificial Intelligence/Machine Learning;
- This includes more efficient, scalable preparation of monomers and polymers using existing or new synthetic routes
- and this call aims to train a technical workforce that leverages data analytics to create sustainable polymers and soft materials.
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Althea Sheets, Research, Scholarly, and Creative Activities Development Manager, Office of Sponsored Programs, althea.sheets@unlv.edu, 702-895-1880
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