The “Computational Inference in High Dimensional Random Complex Systems” Project (COMPREHENSION) is funded by the Ministerio de Economía y Competitividad of Spain.

The term “complex system” is often used to describe a network of elementary units whose collective behavior depends not only on the features of these constituent blocks but also, and specially, on their interactions. The seminal work by Watts and Strogatz (Nature, 1998) sparked a tremendous interest in these kind of large-scale systems. Currently, there exists a wealth of engineering and scientic problems to be addressed related to the modeling, prediction and control of complex systems. To narrow the focus, here we investigate dynamic, high-dimensional and random complex systems and we aim at developing new methodologies for computational inference which are both theoretically sound and practically effective in this setup.While the advance in the theoretical and methodological field is of utmost importance, we also pursue practical applications of the new methods. The most ambitious goal is the modeling of atrial fibrillations (AF) in the human heart; we also investigate relevant problems related to wireless communications and sensor networks (WCSNs), including collaborative routing and distributed implementation of statistical signal processing methods on multi-hop WCSNs. The third axis on which we move from theory to applications deals with environmental applications.