HIGHLIGHTS
Fundamental limits to learning closed-form mathematical models from data
Given a finite and noisy dataset generated with a closed-form mathematical model, when is it possible to learn the true generating model from the data alone? This is the question we investigate here. We show that this model-learning problem displays a transition from a low-noise phase in which the true...
Socially disruptive periods and topics from information-theoretical analysis of judicial decisions
Laws and legal decision-making regulate how societies function. Therefore, they evolve and adapt to new social paradigms and reflect changes in culture and social norms, and are a good proxy for the evolution of socially sensitive issues. Here, we use an information-theoretic methodology to quantitatively track trends and shifts in...
Node Metadata Can Produce Predictability Crossovers in Network Inference Problems
Network inference is the process of learning the properties of complex networks from data. Besides using information about known links in the network, node attributes and other forms of network metadata can help solve network inference problems. Indeed, several approaches have been proposed to introduce metadata into probabilistic network models...
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.