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David Sheen (Fed)

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:

  • National Research Council Postdoctoral Associateship, 2012
  • Dean's Fellowship, University of Southern California, 2005

Other Publications:


Flat-Histogram Extrapolation as a Useful Tool in the Age of Big Data

Nathan Mahynski, Harold Hatch, Matthew Witman, David Sheen, Jeffrey R. Errington, Vincent K. Shen
Here we review recent work by the authors to revisit the concept of extrapolating thermodynamic properties of classical systems using statistical mechanical
Created October 23, 2018, Updated December 8, 2022