HIGHLIGHTS
The importance of being modular
In the 1970s, ecologists began to speculate that modular systems—which are organized into blocks or modules—can better contain perturbations and are therefore more resilient against external damage (1). This simple concept can be applied to any networked system, be it an ecosystem, cellular metabolism, traffic flows, human disease contagion, a...
Accurate and scalable social recommendation using mixed-membership stochastic block models
Proc. Natl. Acad. Sci. USA - Dec. 13, 2016
Recommendation systems are designed to predict users’ preferences and provide them with recommendations for items such as books or movies that suit their needs. Recent developments show that some probabilistic models for user preferences yield better predictions than latent feature models such as matrix factorization. However, it has not been...
Inferring propagation paths for sparsely observed perturbations on complex networks
In a complex system, perturbations propagate by following paths on the network of interactions among the system’s units. In contrast to what happens with the spreading of epidemics, observations of general perturbations are often very sparse in time (there is a single observation of the perturbed system) and in “space”...
OUR RESEARCH
Complex Systems
Cells, ecosystems and economies are examples of complex systems. In complex systems, individual components interact with each other, usually in nonlinear ways, giving rise to complex networks of interactions that are neither totally regular nor totally random. Partly because of the interactions themselves and partly because of the interaction's topology, complex systems cannot be properly understood by just analyzing their constituent parts.
Data Science
Humans generate information at an unprecedented pace, with some estimates suggesting that, in a year, we now produce on the order of 10^21 bytes of data, millions of times the amount of information in all the books ever written. Processing this "data deluge", as some have called it, requires new tools and new approaches at the interface of statistics, statistical and machine learning, network theory and statistical physics.
Multidisciplinarity
Our goal is to push forward the boundaries of science. We are interested in addressing fundamental questions in all areas of science including natural, social and economic sciences. We put a special emphasis in the development of tools that aid scientific discovery through understanding and quantification of a specific phenomenon. To this end we have assembled a multidisciplinary team and have established solid collaborations with experts in biology, social sciences, ecology and economics.