I am a researcher in the fields of Computational Social Sciences, Artificial Intelligence and Complex Systems. I have a PhD in Computer Science and a background of interdisciplinary research. My current appointment is at the Marc Bloch Centre in Berlin.
GraphBrain is a software project aimed at the research of human knowledge as a collective phenomena. More specifically, it focuses on digital media and the application of computational approaches to the study of the relatively recent public space enabled by Internet technologies.
This project is aligned with a fundamentally interdisciplinary research program, that draws from fields such as the Social Sciences, Artificial Intelligence and Epistemology.
In its current state, Graphbrain provides the following:
- An hypergraph knowledge base, capable of representing and efficiently querying complex relationships between entities;
- Knowledge extraction algorithms for popular data sets such as WordNet and DBPedia;
- Entity disambiguation algorithms;
- An entity extraction algorithm for free-form text, such as news items and blog posts;
- A domain specific language to define knowledge extraction rules for free-form text;
- A web-based interface to navigate the knowledge graph.
The main ambition of this software project is to provide a robust base upon which new methods of socio-semantic analysis of digital media can be easily defined. At its core, GraphBrain contains an expressive knowledge representation system that easily lends itself to the organization of entities in terms of relationships, sources and conflicting beliefs.
The GraphBrain knowledge graph is designed to avoid notions of ground truth, instead allowing for the representation of all beliefs as relative to a source. Then, it is possible to define algorithms to determine consensus according to certain criteria or methods. This matches the current situation of digital spaces, where a very large amount of information is available, but knowledge discovery by human actors is constrained by centralized algorithms with poorly understood dynamics (e.g. search engines and recommendation systems).
Beyond the scientific study of collective knowledge phenomena in digital media, GraphBrain could enable the creation of systems of knowledge exploration that are less centralized, less dependent of private actors and more transparent to the users.
Synthetic is a scientific experimentation tool aimed at studying the genesis and dynamics of complex networks using multi-agent simulations and evolutionary computation. Following a standard view in Complexity Science, networks are seen as the emergent outcome of the local, low-level interactions of autonomous agents. The idea of using a multi-agent simulation to model a complex network is not new, but it presents challenges to the human modeler. As simplifying assumptions are removed, or the target network becomes more complex, the discovery of the corresponding low-level mechanism becomes harder. Synthetic aims at aiding and, to a degree, automating this discovery process with evolutionary search.
I have been fascinated by computers from a young age and taught myself to program while in basic school. I have a PhD in Computer Science from the University of Coimbra, with a specialization in Artificial Intelligence. Before, I worked as a software engineer. I developed a software module as part of a larger project for NASA. Later I worked for a mobile search startup company in Cambridge, UK. During my PhD work, I developed a new type of agent controller, called the gridbrain, that allows for embedded evolution in the context of multi-agent simulations. Shortly after finishing my PhD, I moved to Paris, to work as a researcher for the CNRS. There I have been working in an inter-disciplinary environment, exploring ideas like artificial social scientists, digital humanities and knowledge hypergraphs.