Federal agency name: defense advanced research projects agency (DARPA), defense sciences office (DSO) funding opportunity title: young faculty award (YFA) funding opportunity number:…

The Department of the Interior Departmental Offices, Interior Business Center awarded a $500,000 cooperative agreement to Massachusetts Institute of Technology on September 30, 2025 under DARPA's Young Faculty Award program (CFDA 12.910, Research and Technology Development). The award funds development of a formal and practical compositional co-design framework enabling efficient design, operation, and verification of complex adaptive man-machine systems in dynamic environments. The framework addresses computational complexity through novel compositional optimization tools and provides a human-machine interface for decision-making that is intellectually tractable to stakeholders, targeting faster exploration of design decision spaces and improved deployment of autonomous systems in high-stakes defense applications. Performance runs through September 29, 2027 at MIT's location in Massachusetts. The Young Faculty Award solicitation draws from multiple DARPA technical offices—Defense Sciences Office, Information Innovation Office, Microsystems Technology Office, and Tactical Technology Office—and funds innovative research proposals seeking revolutionary advances in science, devices, or systems, excluding evolutionary improvements to existing practice.

Federal agency name: defense advanced research projects agency (DARPA), defense sciences office (DSO) funding opportunity title: young faculty award (YFA) funding opportunity number: DARPARA2501CATALOG of federal domestic assistance (cfda) number(s): 12.910 research and technology developmentprogram description: DARPA is soliciting innovative research proposals in the areas of interest to the following DARPA technical offices: defense sciences office (DSO), information innovation office (I2O), microsystems technology office (MTO), and the tactical technology office (TTO). Further detail regarding the specific technical areas of interest can be found below under open office topics. Proposed research should investigate innovative approaches that enable revolutionary advances in science, devices, or systems. Specifically excluded is research that primarily results in evolutionary improvements to the existing state of practice. This project aims to develop a formal and practical compositional CO-design framework that enables the efficient design, operation, and verification of complex , adaptive man-machine systems in dynamic environments . Traditional approaches struggle with scalability , modularity , and interpretability , limiting their ability to handle heterogeneous , interdependent decisions across multiple conflicting objectives . Furthermore, they often neglect higher-order interactions , compositional (horizontal and vertical) architectures , and trade-offs . Limitations result in costly development cycles, hinder the deployment of autonomous systems in high-stakes defense applications, and fail to provide an intellectually accessible decision-making interface for stakeholders. Our CO-design approach uniquely addresses such challenges by a) tackling computational complexity by enabling faster exploration of design decision spaces via novel compositional optimization tools and b) providing a common interface that is intellectually tractable (human-machine interactions) across a wide range of stakeholders, including technologists , policymakers , and strategists . Specifically, by leveraging applied category theory , domain theory , and compositional optimization , we will develop a new framework for computational CO-design. This will introduce a paradigm shift in the design and deployment of multi-agent autonomous systems, moving from fragmented , domain-specific methodologies to a unified , compositional approach that seamlessly integrates model-based and data-driven tools. Our approach will unlock new capabilities for multi-agent systems, by supporting the synthesis of optimal designs while ensuring their correctness through built-in verification . Results will be tested in simulation and hardware experiments involving robotics and network systems.