(734) 936-7861

Applications:
Biological Sciences, Complex Systems, Earth Science and Ecology, Healthcare Research, Informatics, Social Science
Methodologies:
Causal Inference, Computing, Data Integration, Data Mining, Data Visualization, Databases and Data management, Information Theory, Machine Learning, Mathematical and Statistical Modeling, Optimization, Security and Privacy, Statistics
Relevant Projects:

NSF, NIH-NIGMS, NIH-NIAID


Aaron A. King

Professor

Ecology and Evolutionary Biology

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.