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Charles R Hagwood

Charles Hagwood received his B.S. degree in mathematics from A&T State University, Greensboro, NC. Afterwards, he attended the University of Michigan, graduating in 1979 with a Ph.D. in mathematics, writing a thesis entitled "Discrete Nonlinear Renewal Theory", under Michael Woodroofe. Between 1979 and 1981, Hagwood was a John Wesley Young Research Instructor in the Mathematics Department at Dartmouth College. During 1981-1987, he was an assistant professor in the Mathematics Department at the University of Virginia. In 1984, he received a Ford Foundation Fellowship and spent one year at Stanford University, in the Statistics Department. He has worked at NIST since 1987, providing consulting in areas that include reliability, uncertainty, and stochastic processes.

Technical Areas of Research and Consulting:

  • Stochastic modeling and applied probability

o   Cell biology (segmentation, shape descriptors, nanopores)

o   Aerosol particles (differential mobility analyzer, Langevin nist-equation)

o   Reliability (computer software, conformance tests, materials)

  • Mathematical statistics and applied statistic

o   Standard Reference Materials (certification, uncertainty research, calibration)

o   Consensus values

o   Errors in variables

Professional Activities and Societies and Standards Activities:

  • Institute of Mathematical Statistics, Member


  • Andrew R. Chi Prize Paper Award, 1991  
  • Ford Foundation Fellow, Stanford University, 1984-1985


Measurement of mass of aerosol particles

Kevin J. Coakley, Robert C. Hagwood, Kensei Ehara, Nobuhiko Fukushima, Kittichote Worachotekamjorn, Naoko Tajima, Hiromu Sakurai
An aerosol particle mass analyzer (APM) which classifies aerosol particles according to their mass has been developed. Mass distributions of aerosol particles

Dynamic Spectrum Access Algorithms Based on Survival Analysis

Timothy Hall, Anirudha Sahoo, Robert C. Hagwood, Sarah B. Streett
In this study, we design and implement two algorithms for dynamic spectrum access (DSA) that are based on survival analysis. They use a non-parametric estimate
Created October 9, 2019