(734) 936-7861

Applications:
Bioinformatics, Biological Sciences, Complex Systems, Ecological Research, Epidemiology, Genomics, Population Sciences, Precision Health, Public Health, Research Reproducibility
Methodologies:
Algorithms, Causal Inference, Computational Tools for Data Science, Data Management, Data Munging, Data Security and Privacy, Data Visualization, Database Systems and Infrastructure, Deep Learning, Digital Data Curation, Dynamical Models, Heterogeneous Data Integration, High-Dimensional Data Analysis, Information Theory, Machine Learning, Mathematics, Missing Data and Imputation, Optimization, Predictive Modeling, Signal Processing, Spatio-Temporal Data Analysis, Statistical Analysis and Simulation, Statistical Inference, Statistical Modeling, Statistics, Time Series Analysis
Relevant Projects:

NSF, NIH-NIGMS, NIH-NIAID


Aaron A. King

Professor

Ecology and Evolutionary Biology


Affiliation(s):

Center for the Study of Complex Systems
Mathematics

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.