Caterina De Bacco
The Physics for Inference and Optimization Group’s research focuses on understanding relations between the microscopic and macroscopic properties of complex large-scale interacting systems, such as networks. In cooperation with experts from other disciplines, De Bacco and her team develop models and algorithms based on principles of statistical physics. This knowledge could be used, for instance, to modify the interactions of a network’s individual constituents and thus to optimize its overall properties.
One of the group’s research interests is routing optimization. By collecting data on individual drivers and the vehicles in their immediate surroundings, researchers can draw conclusions on the behavioral patterns of this small group, and use these to make a generalization about all driving behaviors. In other words, they zoom in on part of the whole, closely observe behavioral patterns, and project their findings onto the big picture. Such knowledge can be used to calculate the best possible individual routes for all drivers by optimizing traffic management, even if this may mean a longer distance for the individual. In a collaboration with the Mathematics Department at the University of Padova, De Bacco’s group developed an efficient algorithm capable of deriving optimal general solutions for many routing problems.
De Bacco and her team also focus on investigating inference problems on networks. Inference aims to estimate the parameters of a model that is believed to have generated certain data. De Bacco investigates, for example, how likely it is that members of a social network will interact with one another. In this research area, for instance, she and her group recently developed a model that serves to estimate a node’s measure of importance in a network (known as eigenvector centrality) from a graph sample. This measure is particularly relevant when retrieving the information for the whole network is not feasible, as is the case with social networks.