Tiago de Paula PeixotoComplex Systems and Network Science
Organisms, ecosystems, climates, technological systems, and societies are examples of complex systems: they exhibit rich behavior that emerges from a large number of individual components following relatively simple local rules, based on a network of direct interactions. The 21st century has been marked by the unprecedented volume of digital data being increasingly produced on human behavior, biological systems, economies, and a variety of other complex systems.
Although network theory offers a wide ranging foundation to untangle such intricate systems, potentially allowing us to predict and control their behavior, the analysis of network data is particularly challenging. Since networks are high-dimensional relational objects, low-order statistics can reveal only very little about them. Conversely, higher-order representations need to be obtained according to principled methodology to avoid misleading characterizations and statistical illusions.
It is the aim of the “Inverse Complexity Lab” is to develop mathematical and computational models to explain the structure and function of complex network systems, and the algorithms to reconstruct the structure of these models from available empirical data. The methodological research is inherently integrated with in-depth applications in a variety of scientific domains, including epidemiology, neuroscience, microbial ecology, and computational social science.
Members of this research group bring together theory and methods from several disciplines, including statistical physics, computational statistics, information theory, Bayesian Inference, and machine learning.
