Many complex engineering systems involve interactions among a large number of agents with coupled dynamics and decisions due to their shared environment and resources. Such systems are often operated using a hierarchical architecture, where a coordinator determines some macroscopic control signal to steer the population to achieve a desired group objective while respecting local preferences and constraints for individual agents. Examples include electricity demand response programs, ground and air transportation systems, data center power management, robotic networks, among others. The goal of this project is to establish new control and game theoretic foundations, along with numerical algorithms, to enable formal and scalable design of hierarchical population control systems. In contrast to the existing literature that primarily focuses on static strategic agents, this project will consider both strategic and non-strategic agents with nontrivial dynamics. The project involves three tasks. (i) First, it will establish control theoretic foundations for hierarchical population control of non-strategic agents (HPCN). Each non-strategic agent is associated with a predefined local response rule and is modeled as a hybrid system. A novel approach based on abstraction of stochastic hybrid systems (SHS) will be investigated to solve the HPCN problem. (ii) The project will also develop a uniform-price dynamic mechanism design framework for hierarchical population control of strategic agents (HPCS). The framework is based the near-Nash equilibrium concept that can facilitate the analysis of the game-theoretic population behaviors. Advanced bi-level optimization algorithms will also be developed to address the computational challenges associated with the proposed mechanism design approach. (iii) The two proposed hierarchical population control frameworks will be used to study important demand response applications for the future power grid. This research will significantly advance our understanding in complex engineering systems that involve coordination of a large population of dynamic agents. In collaboration with the Pacific Northwest National Laboratory, the project is also expected to yield practical algorithms and numerical tools for the design of electricity demand response programs. Moreover, the project will impact several education activities such as use of new pedagogical tools in teaching, involvement of undergraduate students in research, and research integration with teaching.