NIST only participates in the February and August reviews.
Computational tools for analysis and model development have been widely used in many applications, but the predictions of these models are not complete without an estimate of the uncertainty in those predictions. With uncertainty analysis, the model predictions are made more robust and reliable. This project seeks to understand and develop techniques for uncertainty analysis in machine learning and advanced optimization for fields including precision medicine, biomanufacturing, omics research, and kinetic model development. Techniques for uncertainty estimation will range from non-parametric methods such as bootstrapping to fully Bayesian analysis.
W. F. C. Rocha, D. A. Sheen, SAR and QSAR in Environmental Research (2016), 799-811.
D. A. Sheen, W. F. C. Rocha, K. A. Lippa, D. W. Bearden, Chemometrics and Intelligent Laboratory Systems 162 (2017), 10-20.
Machine learning; Outlier detection; Optimization; Informatics; Chemometrics; Metabolomics; Bayesian analysis; Uncertainty analysis; Uncertainty quantification
Find and choose an agency to see details and to explore individual opportunities.