Prof. Walczak’s research is at the interface of statistical mechanics and immunology, including gene regulatory networks, collective behavior of the immune system and population genetics. Her lab describes systems at the cellular scale, understanding links between function, development, and evolvability of conserved pathways and their elements. They use experimental data to learn probabilistic models and the immune system, and develop theoretical descriptions to see how interactions between molecular components in the cell influence phenotypes.

Prof. Mora studies the behavior of complex biological systems that show interesting emergent phenomena in immunology, neuroscience, cellular biology, and collective behavior. He combines a bottom-up approach, in which mechanisms of organization are hypothesized from efficient design principles, and top-down approach, where local rules of interaction are learned from data using statistical learning and statistical physics techniques. His lab focuses on model systems where collective effects play important role.

Prof. Allesina is a theoretical ecologist studying mostly ecological networks. He is interested in the dynamics of large ecological communities, models for network structure, and the response of ecological communities to extinctions. His laboratory develops mathematical, computational and statistical tools for the analysis of ecological systems.

Prof. Cobey investigates the coevolution of pathogens and hosts’ adaptive immunity. Her group uses computational and mathematical tools to test hypotheses and to advance theory. Their earlier work focused on the evolutionary consequences of immune-mediated competition between different pathogen strains, including pneumococcus, human papillomavirus, and especially influenza. More recently, the focus has expanded to include the related dynamics of the host immune response.

Prof. Stephens works on a wide variety of problems at the interface of statistics and genetics. His lab often tackles problems where novel statistical methods are required, or can learn something new compared with existing approaches. Much of that research involves developing new statistical methodology, many of which have a non-trivial computational component. And because data sets are getting larger and larger, that work often involves modern methods for “high-dimensional statistics”. The lab also makes extensive use of Bayesian hierarchical models to borrow information across data sets or sampling units.