This project focuses on designing control mechanisms for a networked system with unknown structure by making use only of non-invasive observations. By non-invasive observations, it is meant that what is being measured is not the system reaction to actively injected inputs, but rather the system behavior when it is operating under standard conditions and subject to potentially unobservable forcing signals. The capability of designing controllers based only on non-invasive observations is of paramount importance for any large scale network fulfilling critical or uninterruptible functions (i.e., a power grid, a logistic system) or in situations where it is impractical or too expensive to inject known probing signals into the system (i.e., a gene network, a financial network). Other relevant applications are in medicine (i.e., repeated drug testing, computer- assisted anesthesia). Indeed, in these cases, for obvious safety and health concerns, it is not desirable to actively test the response of a patient to a different drug dosage or treatment, if comparably useful information could be inferred from non-invasive observations. Since non-invasive observations do not always provide full information about the network's configuration, the project will also consider how to define adequate control mechanisms that are robust with respect to uncertainties in the connectivity structure. These kinds of uncertainties are not typically considered in standard techniques for control design and the development of specific methodologies is required. Combined with the capability of adapting to changes in the network's configuration, these control techniques will provide a solid foundation for the realization of a self-healing system. This project will bridge together different areas, including statistics, computer science, and control theory with a single unifying framework. New courses will be created to facilitate communication among all these communities of researchers, advancing separate fields in a multidisciplinary way.