Distributed control is a classical research topic. While a rich theory is available, some assumptions such as availability of subsystem dynamics and topology and the subsystems following the prescribed controllers exactly have proven difficult to remove. An interesting direction in recent times to get away from these assumptions has been the utilization of learning for control. In this talk, we consider some problems in control design for distributed systems using learning. Our core message is that utilizing control-relevant properties in learning algorithms can not only guarantee concerns such as stability, performance, safety, and robustness that are important in control of physical systems, but also help with issues such as data sparsity and sample complexity that are concerns during the implementation of learning algorithms. Co-sponsored by: Temple University Electrical and Computer Engineering Department Speaker(s): Vijay, Virtual: https://events.vtools.ieee.org/m/432380