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 communication systems,, as well as economies and political deliberation processes.
At the Complex Adaptive Systems and Computational Intelligence (CASCI Lab), we are particularly interested in multilevel network properties of natural and artificial systems which enable them spread and process information, adapt, and evolve. This means both understanding how redundancy is fundamental for controlling the behavior and evolvability of complex systems, as well as producing actionable computational models of biomedical and social phenomena---often by abstracting principles from natural systems. This theoretical and applied research agenda focuses on tackling multilevel complexity involved in human health and society , and is organized in three main threads detailed below: complex networks & systems, Computational & Systems Biology, and Computational Intelligence. We are very collaborative, being involved in many interdisciplinary projects with other teams. We are always looking for postdocs and students at any level to collaborate with us, please see how to join us.