Almost all interesting processes in nature and society are highly cross linked. In many systems, however, we can distinguish a set of fundamental building blocks, which interact nonlinearly to form compound structures or functions with an identity that requires more explanatory devices than those used to explain the building blocks. Multivariate systems that need complementary, multi-level modes of description are defined as complex systems. They are typically modelled as networks or dynamical systems. Examples abound: gene networks that direct developmental processes under selective pressure; immune networks that preserve the identity of organisms; social insect colonies; neural, physiological, and technological networks that produce intelligence; ecological networks; social networks comprised of transportation, utilities, and telecommunication systems, as well as economies and political deliberation processes.
We are particularly interested in the informational properties of natural and artificial systems which enable them to adapt and evolve. This means both understanding how information is fundamental for controlling the behavior and evolutionary capabilities of complex systems, as well as producing actionable computational models of various social, technological, and biomedical phenomena---often by abstracting principles from natural systems. This theoretical and applied research agenda focuses on tackling multi-level complexity involved in human health, and is organized in three main threads detailed below: complex networks & systems, Computational & Systems Biology, and Computational Intelligence. Please also check the research group on Complex Adaptive Systems and Computational Intelligence (CASCI) I lead, for more details about our research and how to collaborate and study with us.