My work focuses on the analysis of distributed complex systems, addressing problems at the interface between computational neuroscience, complexity science, and machine learning. In general terms, my work aims to reveal the fascinating principles that drive the computations performed by distributed multi-agent systems: how can they become more than the sum of their parts, perform tasks collectively that couldn't be achieved individually, and even give rise to novel features that were not predefined beforehand. These are exciting times to pursue these questions, as theoretical approaches can now be complemented by empirical investigations based on big data.
On the theoretical side, my research focuses on formulating novel mathematical frameworks based on foundational but elusive intuitions about how information is processed in distributed resource-constrained systems, which in turn lead to algorithms that enable new avenues of practical data analysis. My vision is that such analysis frameworks, which go beyond mere data fitting to truly reveal the inner workings of complex distributed systems, will be instrumental for future scientific advancements and technological breakthroughs. I am particularly interested in the potential of these tools to deepen our understanding of the inner workings of the human brain, and also our understanding of social scenarios involving collective experiences. I strongly believe that insightful science can lead us to a better world where humans are empowered by novel technologies that provide means for them to thrive and foster their creativity and well-being.