Cyber-physical systems (CPS) form the foundation of modern intelligent infrastructure by tightly integrating physical processes with computation, communication, and control. The increasing complexity of CPS, particularly in transportation, robotics, disaster response, which are focal domains of this project, necessitates advanced decision-making frameworks capable of modeling and optimizing hierarchical, interactive behaviors under uncertainty. As urban transportation systems become more connected and autonomous, and as robotics platforms evolve toward multi-agent, distributed architectures, the role of bilevel optimization becomes particularly salient. This project will build a novel, scalable, and trust-aware bilevel optimization framework tailored for multi-user CPS operating under uncertainty and ambiguous user trust. By explicitly embedding human trust and behavioral uncertainty into optimization models, the research aims to advance the design of smarter, safer, and more adaptive CPS. The outcomes have the potential to transform sectors where human-machine interaction plays a central role. Beyond research contributions, the project will support education and workforce development through interdisciplinary student training, the creation of interactive games to introduce K–12 students to human-in-the-loop control and optimization, and the development of new graduate-level courses on CPS optimization and computation, which will be made available as open-access online conten