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The Statistical Engineering Division:
- develops and applies best practices for the characterization of measurement uncertainty, in particular to enable the intercomparison of measurements in the context of interlaboratory studies and calibrations;
- implements methods and techniques for experimental design, data analysis, statistical modeling and probabilistic inference in computer software;
- disseminates such methods and techniques throughout U.S. industry, and the scientific and academic communities at large, by publishing technical and educational materials in print and on-line, by offering training courses and workshops, and by participating in professional conferences.
The SED of the ITL of the NIST conducts fundamental and applied statistical research on problems in metrology and collaborates on research in other Divisions of ITL, in other Laboratories of NIST and with NIST's industrial partners.
As of July 2014 Antonio Possolo has been promoted to the rank of NIST Fellow. The honorific "NIST Fellow" is the highest technical rank that NIST can bestow, and this honor recognizes Antonio's contributions and leadership in a wide range of NIST mission areas. As part of his new duties Antonio will serve as the Chief Statistician for NIST.
Nien-fan Zhang was recognized with a 2013 R&D 100 Award, for joint work with Bryan Barnes, Rick Silver, and Hui Zhou for developing a Quantitative Hybrid Metrology (QHM) system that is already being used in optical measurement. Called the "Oscars of Innovation", the R&D 100 Awards recognize and celebrate the top 100 technology products of the year. QHM is the first technology that rigorously combines two or more different measurement techniques into an integrated procedure achieving reduced overall measurement uncertainty and optimized measurement throughput.