Prediction and Inference in Complex Systems

Complex systems are made up of discrete parts that interact and form a

network. The functioning and evolution of these systems depend on the network's structure. For example, infectious diseases spread due to the connections between individuals. Our brain synchronizes according to the structure of connections between neurons. Since network structure affects these dynamic processes, we can use statistics and machine learning methods to make predictions and inferences about dynamic variables. In this talk, we will discuss how to predict dynamic processes, such as epidemics and synchronization, and infer the influence of the network on such processes.