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The proliferation of mobile and Internet-of-Things (IoT) devices, and their pervasiveness across nearly every sphere of our society, continues to raise questions about the architectures that organize tomorrow’s compute infrastructure. At the heart of this trend is the data that will be generated as myriad devices and application services operate simultaneously to digitize a complex domain like a smart building or smart industrial facility. A key shift is from edge devices consuming data produced in the cloud to edge devices being a voluminous producer of data. This shift reopens a broad variety of system-level research questions concerning data placement, movement, processing and sharing. Importantly, the shift also opens the door to compelling new applications with significant industrial and societal impact in domains such as healthcare, manufacturing, transportation, public safety, energy, buildings, and telecommunications. Edge computing is broadly defined as a networked systems architectural approach in which compute and storage resources are placed at the network edge, in proximity to the mobile and IoT devices.

The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and the Simons Foundation Division of Mathematics and Physical Sciences will jointly sponsor up to two new research collaborations consisting of mathematicians, statisticians, electrical engineers, and theoretical computer scientists. Research activities will be focused on explicit topics involving some of the most challenging questions in the general area of Mathematical and Scientific Foundations of Deep Learning. Each collaboration will conduct training through research involvement of recent doctoral degree recipients, graduate students, and/or undergraduate students from across this multi-disciplinary spectrum. Annual meetings of the Principal Investigators ("PIs") and other principal researchers involved in the collaborations will be held at the Simons Foundation in New York City. This program complements NSF's National Artificial Intelligence Research Institutes program by supporting collaborative research focused on the mathematical and scientific foundations of Deep Learning through a different modality and at a different scale.

The Engineering Design and Systems Engineering (EDSE) program supports fundamental research into the basic processes and phenomena of engineering design and systems engineering. The program seeks proposals leading to improved understanding about how processes, organizational structure, social interactions, strategic decision making, and other factors impact success in the planning and execution of engineering design and systems engineering projects. It also supports advances pertaining to engineering design and systems engineering in areas that include, but are not limited to, decision making under uncertainty, including preference and demand modeling; problem decomposition and decision delegation; applications of reverse game theory (mechanism design); computer-aided design; design representation; system performance modeling and prediction; design optimization; uncertainty quantification; domain- or concern-specific design methods; and advanced computational techniques for supporting effective human cognition, decision making, and collaboration. Competitive proposals for novel methods will include a plan to evaluate rigorously the effectiveness and performance of the proposed approach.

July 16 | 3:00-4:30pm ET The IUSE: EHR program seeks to promote novel, creative, and transformative approaches to generating and using new knowledge about STEM teaching and learning to improve STEM education for all undergraduates. The program supports proposals interested in improving undergraduate education, developing faculty expertise, preparing K-12 teachers, and providing all undergraduate students with STEM competencies and a basic understanding of STEM concepts and principles. All projects must contribute to the body of knowledge about what works in undergraduate STEM education and the conditions that lead to improved STEM teaching and learning.