The long temporal and large spatial scales of ecological systems make controlled experimentation difficult and the amassing of informative data challenging and expensive. The resulting sparsity and noise are major impediments to scientific progress in ecology, which therefore depends on efficient use of data. In this context, it has in recent years been recognized that the onetime playthings of theoretical ecologists, mathematical models of ecological processes, are no longer exclusively the stuff of thought experiments, but have great utility in the context of causal inference. Specifically, because they embody scientific questions about ecological processes in sharpest form—making precise, quantitative, testable predictions—the rigorous confrontation of process-based models with data accelerates the development of ecological understanding. This is the central premise of my research program and the common thread of the work that goes on in my laboratory.

I have devoted myself to asking fundamental questions in new ways and to devising and disseminating novel methods by which existing data can answer these questions. Most notably, our work has opened up new vistas in the ecology and evolution of infectious disease, but has had impacts on other areas of ecology and evolutionary biology as well.

In the King Lab, we use sophisticated mathematical, computational, and statistical tools to advance our theoretical understanding of ecological and evolutionary processes. We formalize scientific hypotheses as mathematical models to make precise predictions and powerful inference. One major focus of our research is the ecology and evolution of infectious diseases. We formulate mathematical models and confront them with data to learn about the mechanisms that operate in the host-pathogen interaction and about how they are likely to evolve. Students and postdocs in the lab have a wide range of interests; the common thread is the use of rigorous theoretical approaches on fundamental questions in ecology and evolutionary biology.

The lab accepts graduate students through Ecology & Evolutionary Biology, Applied & Interdisciplinary Mathematics, Bioinformatics, and Data Science. Students interested in applying should have a strong quantitative background, some experience with scientific computation, and a burning interest in developing and testing theory for real ecological systems. Please contact Prof. King if you are interested in joining us.