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in various research activities related to computing and information technology. Specific areas of interest include intelligent medical devices; signal/data collection and processing from various
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the rigorous data quality metrics for accurate diagnostics, prognostics, medical biomarkers, and for untangling the mechanisms of disease. The development and delivery of solution-enabling metrology tools
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output properties. These approaches often ignore material characterization information and/or proper information-rich AI surrogate models (thereby having no capability for uncertainty quantification (UQ
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mechanical parts and assemblies (SolidWorks experience is preferable), experience with developing VI’s in LabVIEW, and experience with data and image processing (MATLAB experience preferable). [1] Huang, W
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andrei.kazakov@nist.gov 303.497.4898 Description Empirical correlations derived from existing experimental data have always played an important role in thermophysical property estimation. These empirical
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performance modeling capabilities that simultaneously consider multiple performance aspects, robust IAQ and other performance metrics, and measurement methods, sensors, and data to evaluate and verify building
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machine tools that are self-aware via on-machine measurements and diagnostics, to track the machine’s performance health. Furthermore, solutions must be non-invasive, data-rich, inexpensive, and accurate
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of mixed-gas adsorption, and the measurement of related physicochemical properties of porous materials. The FACT Lab seeks to improve the adsorption sciences by providing reference data using reference
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thermophysical property data for the process gases. Our group maintains a wide variety of experimental apparatus for state-of-the-art property measurements. These include a two-sinker densimeter for reference
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the continuing increase in speed and capability of commodity graphics processors, immersive visualization offers increasing opportunities to express scientifically meaningful results. Data at NIST spans a wide