Fernando is a researcher who uses applied mathematics, statistical physics, and topology methods to understand the emergence of complex systems in a broader sense, from brains to society. His work at the RPA PSI focuses on leveraging a range of advanced tools. He uses multilayer analysis to integrate diverse data sources into a single cohesive framework; high-order methods to move beyond simple pairwise comparisons and uncover subtle, group-level connections; renormalization techniques to examine how patterns scale as we zoom in and out of different societal levels; and statistical mechanics to model and predict the behavior of complex, interacting elements within large populations.

By looking at data more holistically, he aims to help the RPA build realistic models of social systems, drawing on large-scale datasets from the Dutch Central Bureau of Statistics (CBS). These techniques can capture how communities form and dissolve, how opinions spread and polarize, and how inequalities emerge. In addition, he is committed to open science. Fernando would like to work to make the insights and tools we develop to work with CBS within the RPA accessible, so that researchers, policymakers, and community members can benefit. Through his role, he strives to strengthen the bridge between CBS and the University of Amsterdam, making valuable societal data usable for everyone and encouraging an open, collaborative research environment.