Resources in Computation

Computer programs constantly bump up against hard limits on memory and processing power, yet computer scientists lack a single, unified way to think about these constraints. This matters because the field currently treats resource limits in two disconnected ways. One approach focuses on precise, quantitative questions—how much time or memory a program needs to run. The other deals with conceptual abstractions that underpin modern programming languages, such as how to manage data ownership or parallel tasks. These two worlds rarely talk to each other, which slows progress. A programmer who wants to write efficient code for a smartphone, for instance, cannot easily draw on theoretical insights about resource management from high-level language design. This project aims to build a unified theory of resources that bridges that gap. If it succeeds, computer scientists will gain new tools and methods that apply across both quantitative and conceptual domains. The work is fundamental science—it will not produce a new app or faster processor tomorrow. But similar foundational work on computational complexity and type theory in past decades eventually shaped everything from database systems to secure software. A deeper, more general understanding of how programs manage resources could, over time, lead to more efficient compilers, better programming languages, and systems that make smarter use of the hardware they run on.