From cancer treatment to swarm robotics, many emerging technologies rely on understanding and controlling large populations of interconnected systems. However, these populations are often highly diverse, and scientists and engineers typically cannot observe or control individual components directly. Instead, they must rely on “unfocused” interactions, such as measurements that provide only average behaviors across the entire group or control inputs that affect all members simultaneously. This project addresses the fundamental challenge of making precise, targeted interventions in these complex populations using only broad, population-level information. By developing new theoretical frameworks and data-driven tools, this research aims to unlock a powerful form of population-level feedback control, closing the loop not on individuals, but on the collective. This work has the potential to transform a wide range of applications, from improving the effectiveness of cancer therapies that must target heterogeneous cell populations, and designing intelligent control for robotic swarms, to optimizing large-scale infrastructure systems. Beyond its direct research goals, the project contributes broadly to the scientific community by providing open-source tools, hosting educational workshops, and creating research opportunities for K-12 students and the general public. This research introduces a novel computational and theoretical framework for controlling dynamic ensemble systems us