Natural and engineered systems that consist of populations of isolated or interacting dynamical components exhibit levels of complexity that are beyond human comprehension. These complex systems often require an appropriate excitation, an optimal hierarchical organization, or a periodic dynamical structure, such as synchrony, to function as desired or operate optimally. In many application domains, e.g., neurostimulation in brain medicine and nuclear magnetic resonance spectroscopy and imaging in quantum control, control and observation can only be implemented at the population level, through broadcasting a single input signal to all the systems in the population and through collecting aggregated system-level measurements of the population, respectively. These limitations give rise to challenging problems and new control paradigms involving underactuated manipulation of dynamic ensembles. This talk will address theoretical and computational challenges for targeted coordination of both isolated and networked ensemble systems arising in diverse areas at different scales. Both model-based and data-driven approaches for learning, decoding, control, and computation of dynamic structures and patterns in ensemble systems will be presented. Practical control designs, including synchronization waveforms for pattern formation in complex networks and optimal pulses in quantum control, will be illustrated along with their experimental realizations. Lastly, future directions and opportunities in Systems and Controls will be discussed.