NSF CoDec will develop a new class of computational techniques, algorithms, systems, and AI methods to sense and optimize efficiency and resilience of societal infrastructure over intermediate time scales of minutes-to-years and spatial scales of communities-to-countries.
CoDec recognizes the unique role computing will play in transforming society to automate and coordinate various efficiency optimizations across time, space, and sectors, and will consume increasingly significant amounts of energy but has substantial temporal, spatial, and performance flexibility.
NSF CoDec includes two foundational research thrusts and three use-inspired research thrusts that are tightly integrated with each other.
FOUNDATIONAL
Theory & AI
This thrust designs general theory and AI approaches for optimizing efficiency and resilience at mesoscales, including learning-driven online optimization, optimization-in-the-loop learning, and multi-agent learning, while also considering economic incentives.
FOUNDATIONAL
Systems
This thrust designs general software platforms and energy services for improving distributed infrastructure systems’ visibility, flexibility, and programmability to monitor and respond to changes in electric grid conditions and demand.
Use-Inspired
Computing & AI
This thrust adapts our foundational thrusts to optimize energy efficiency and resilience for AI applications, large-scale cloud platforms, edge networks, and client devices, while also extending equipment lifetimes.
Use-Inspired
Societal Infrastructure
This thrust applies our foundational thrusts for lifecycle optimizations of other critical large-scale societal infrastructure, including the built environment, electric transportation networks, and human-in-the-loop systems.
Use-Inspired
Coupled Infrastructure
This thrust leverages both our foundational thrusts and our insights above to develop new approaches for cross-domain optimizations between the grid, computing, and domains such as buildings and transportation.
