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John Lu (Fed)

John Lu grew up in northern Anhui Province, China before he went to Beijing to study at the Peking University where he received a BS in mathematics with specialization in probability and statistics in 1986. He came to the US to study at the University of North Carolina, Chapel Hill, and received MS and PhD in statistics, under the guidance of Richard L. Smith. He was a visiting scientist at National Center for Atmospheric Research (NCAR), Boulder, Colorado, where he did post-doctoral research on applying Bayesian statistics to atmospheric sciences under the mentorship of professor Mark Berliner from the Ohio State University. He spent a year as a visiting assistant professor at the Hong Kong University of Science and Technology in Kowloon, Hong Kong, China in 1997-1998.  

He was a research scientist doing S-plus statistical software development and research from 1999-2001. He has been at NIST as a mathematical statistician since July 2001. His main research activity has been in interdisciplinary statistical research, working closely with other discipline scientists  to address various measurement science problems arising from physical, phantom, or medical products. His experiences of consulting and research cut across many application areas including climatology, material sciences, biotechnology, chemical sciences, information technology, manufacturing, and physical sciences.

Ongoing or past research projects or interest:

  • Statistics for optical imaging and forensics
  • High-dimensional statistics and shape metrology
  • Statistical methods and analysis of data as curves or distributions
  • Bayesian statistics and applications
  • Nonlinear and nonparametric regression
  • Time series analysis
  • Collaborative research, applications of statistics to materials science, physical sciences, biosciences, medicine,  and information technology

Professional Activities and Societies and Standards Activities:

  • Institute of Mathematical Statistics, Life Member
  • Society for Industrial and Applied Mathematics, Member
  • RSNA QIBA Volumetric Committee

Selected past publications prior to joining NIST:

  • Z. Q. Lu (2002) Local Polynomial Prediction and Volatility estimation in Financial Time Series, in Modeling and Forecasting Financial Data: Techniques of Nonlinear Dynamics, (Studies in Computational Finance), editors, Abdol S. Soofi, Liangyue Cao, Springer (Kluwer Academic Publishers), 2002, Part II, Ch. 5, pp 115-135.
  • R. M. Errico, L. Fillion, D. W. Nychka, Z. Q. Lu (2000) Some statistical considerations associated with the data assimilation of precipitation observations. Quarterly Journal of the Royal Meteorological Society. Vol. 126, no. 562, part A 339-360.
  • Z. Q. Lu, L. M. Berliner, C. Snyder (2000) Optimal design for spatial and adaptive observations. Studies in Atmospheric Sciences, Lecture Notes in Statistics, Vol.144, Springer-verlag, 2000. pp 65-78.
  • Z. Q. Lu (1999) Multivariate local polynomial fitting for martingale nonlinear regression models. Annals of Institute of Statistical Mathematics, Vol.51, No.4, 691-706, December, 1999.
  • L. M. Berliner, Z. Q. Lu, C. Snyder (1999) Statistical Design for Adaptive Weather Observation. Journal of the Atmospheric Sciences: Vol. 56, No. 15, 2536-2552, August, 1999.
  • Z. Q. Lu (1999) Nonparametric Regression with Singular Design. Journal of Multivariate Analysis, Vol.70, 177-201, 1999.
  • Z. Q. Lu, L. M. Berliner (1999) Markov switching time series models with application to a daily runoff series. Water Resources Research, Vol. 35, No. 2, 523-534. Feb. 1999.
  • Z. Q. Lu, R. L. Smith (1997) Estimating Local Lyapunov Exponents.  In Nonlinear Dynamics and Time Series,Building a Bridge Between the Natural and Statistical Sciences,  editors Colleen D. Cutler and Daniel T. Kaplan. 135-151. Fields Institute Communications, Vol. 11, American Mathematical Society, 1997.
  • Z. Q. Lu (1996) Multivariate locally weighted polynomial fitting and partial derivative estimation. Journal of Multivariate Analysis, 1996, 59, 187-205.

Selected Publications


Created October 9, 2019, Updated December 8, 2022