David's expertise is in model development and uncertainty analysis, including Bayesian model updating and experimental design. He has applied these techniques primarily to detailed modeling of chemical processes such as high-temperature hydrocarbon oxidation, where a model with a large number of estimated but uncertain parameters must be constrained against experimental measurements with uncertainty. As part of this research, he has developed models for the oxidation of ethylene and heptane as reference fuels, combining measurements from a variety of sources into a single cohesive model. At NIST, he has created unified models for the decomposition of hydrocarbons based on a range of rate measurements for different substances. Lately, his research is extending into uncertainty analysis for process control and automated substance identification in metabolomics.
Professional Awards and Recognition: