About me


Principal Research Scientist at Redzone and Visiting Scholar at MIT Media Lab
Named Innovator Under 35 by MIT Technology Review en Español in 2018.

Passionate in understanding complex, social behaviors by analyzing (big) data from social media and alternative sources, using networks, AI and machine learning.

Analysis of social dynamics across scales: from global societies down to cities and organizations.  Transforming data into visualization, understanding and strategy.

Visit Research for more information.

Unveiling the power of data

We live in a complex world overwhelmed by data. We need better tools than traditional statistics to unleash their hidden power. Artificial intelligence and complexity science let us unveil unstructured patterns of behavior. Rather than trusting a black box, we show what the data has to tell.

Who can benefit?

Planning offices, Companies and NGOs who need to understand the behavior of societies:  mapping poverty and segregation. planning urban dynamics, assessing natural disasters, observing team dynamics, and customers’ behavior.

Create the value of data

Untitled 6
Visualization of ethnic segregation and geographic segmentation of Ivory Coast.


Complex systems: Data and Society

In our modern world, we constantly interact with electronic devices, such as
mobile phones, e-mail or online social networks. The increasing integration of technological solutions into people’s life, is certainly affecting the way people relate to each other and consequently the properties of the social system. Historically, the exchange of information among social groups has influenced and determined the course of events across societies. In fact, the development of societies is associated with the number and diversity of exchanging connections. The recent explosion of information technologies has enabled the emergence of a global society without precedents. One society in which distances no longer exist and where previously isolated events may trigger worldwide reactions in a few instants.

Researchers say that the world is heading towards a networked society, where Internet based solutions are emerging as alternatives to traditional centralist institutions. For instance, social media allows people to broadcast information extremely affordable in a
global scale, in detriment to mass media companies, which no longer control the monopoly of information. Also, the large number of collaborative online working sites and the increasing activity of international freelancing, are signs that corporations are no longer needed to conduct large business. In fact, new virtual currencies are already working as an alternative to traditional financial systems, waving intermediaries and international monetary institutions.

As a consequence, current business and political models must choose either adapting to the new times or becoming extinct.

The current challenge is to characterize the social systems that emerge from these new
technological spaces and to understand their rules of behavior [LPA+09]. For this purpose, we must enhance our ability to measure these systems in their actual dimensions. Fortunately, the mentioned explosion of information technologies is providing the data required for these analyses. When consuming these services, we are unconsciously leaving traces of our activity as a by-product in the providers’ databases. Individually, these records contain detailed information about the user activity and may serve for billing processes. It is natural to think that users will have a unique profile determined by their own habits and customs. However, these databases are so large that they have the dimensions required to enable the observation of large scale human behavioral patterns. In fact, they are unveiling the characteristics of societies as a whole physical system, rather than a collection of isolated individuals. Besides, these datasets have the advantage of being real measurements of people’s actual behavior, instead of the result of some sparse observations and honesty based questionnaires.

The human society is a complex system. Many social phenomena strongly depend upon
the way people behave and how collective actions are combined together in the society. Like in other complex systems, there are global properties that emerge from the
relationships between the individuals, rather than the properties of the individuals themselves. The elements in complex systems do not behave independently from each other but neither behave fully coherently. Instead, individuals create interdependencies in their actions, given in the form of collective behaviors. During this process, the individuals lose independence in their behavior, in favor for the system to gain properties and capabilities at larger scales. As a result, the emergence of a collective behavior increases the system’s complexity.

In the case of today’s societies, we can find traces of the collective behavior that enables
larger scale patterns in the data derived from human activity. That information is embedded and unstructured in the raw data. Therefore, in order to retrieve this knowledge, we must treat the data properly [BYB13]. On the one hand, we can not explain the system through the individual states, since it would require as many descriptors as to make the system too complicated to understand. But, on the other hand, we can not reduce the overall behavior into mere statistics either. By doing this, we will lose the heterogeneity and diversity typical of social systems. Therefore, we need frameworks to observe the system at all its complexity.

Complex systems provide tools to treat and analyze this kind of systems. Networks are mathematical structures compound by a set of nodes, linked to each other by a set of edges that represent relationships or interactions between the systems’ elements. By analyzing systems in the form of networks, we can understand their structure, their dynamical evolution and the responsible mechanisms for patterns formation.

In general, networks are a common ground for analyzing complex systems in a variety of scientific disciplines. In part because networks reveal the systems’ characteristics across several scales. They can describe the systems’ global properties and their functioning as a whole . At the same time, they can also describe local interactions, the role of individuals in their environment and the connection patterns, which include structures at intermediate scales.

Recently, there has been an explosion of research for ways to retrieve societal knowledge
from data. Most of these studies take advantage of the size, diversity and real-time nature
of the data in order to revise old sociological questions and to ask new ones. Such way
of studying social systems is unprecedented and it is revealing the true nature of societal
phenomena. For instance, patterns in the diversity of connections can explain the economical development of cities, as well as the emotional state of individuals. Also,
patterns of popularity can explain the economical value of stocks or earthquake
epicenters. Finally, patterns of mobility can predict the propagation of infectious
diseases or evaluate urban land use.

Further from remarkably increasing our societal knowledge, these and many other scientific advances suggest that the analysis of data can be incorporated as valuable information for decision making and policy evaluation processes, in both private and public sectors. First because the analysis of data has the potential to show an unprecedented view of the impact of policies on the population, so that they can be revised and modified if needed. Second because they can also provide the knowledge to rethink the way our social and engineered systems are functioning together, in order to design new rules of complex interactions for building better societies in the future.