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Amanda Koepke (Fed)

Mathematical Statistician

Amanda Koepke (she/her/hers) graduated from Texas Tech University in 2008 with a B.A. in Mathematics and Statistics and a B.A. in Psychology. In 2014 she received her Ph.D. in Statistics from the University of Washington. She completed a year-long postdoctoral research fellowship at the Fred Hutchinson Cancer Research Center in Seattle before joining the Statistical Engineering Division at NIST in 2015.

Amanda's current research interests include Bayesian analysis, meta-analysis/interlaboratory studies, and spectral analysis. She is participating in the ITL Speakers Bureau: https://www.nist.gov/itl/itls-speakers-bureau-amanda-koepke

 

Technical Areas of Research and Consulting

  • Bayesian analysis
  • Statistical modeling
  • Uncertainty analysis
  • Markov chain Monte Carlo methods
  • Consensus values
  • Spectral analysis

 

PROFESSIONAL ACTIVITIES AND SOCIETIES 

Awards

  • NIST Bronze Medal (2023), for development and deployment of a statistical tool for experiment design and rigorous assessment of measurement uncertainty for chemical analyses.
  • 2023 ITL Outstanding Technology Transfer Award, for development and deployment of a statistical tool for experiment design and rigorous assessment of measurement uncertainty for chemical analyses.
  • NIST Recommendation for Recognition (2023), for transferring statistical and chemical expertise to the metrology community even though that required a computational approach not commonly used in chemistry due to its complexity, computational demands, and lack of familiarity.
  • 2021 ITL Outstanding Contribution to Enhance Diversity Award, for contributions to an exceptional range of NIST diversity programs, including leadership in outreach and commitment to improving inclusivity.
  • NIST Recommendation for Recognition (2020), recognizing "significant contributions to gender diversity" through identifying and understanding the trends in 10 years of NIST HR data.
  • 2015 Young Investigator Award from the Statistics in Epidemiology Section of the American Statistical Association.

Publications

Decision Tree for Key Comparisons

Author(s)
Antonio Possolo, Amanda Koepke, David Newton, Michael R. Winchester
This contribution describes a Decision Tree intended to guide the selection of statistical models and data reduction procedures in key comparisons (KCs). The
Created June 5, 2018, Updated February 22, 2024