John Lu (Fed)

John Lu grew up in northern Anhui Province, China and attended the Peking University where he received a BS in mathematics with specialization in probability and statistics. 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 the first post-doc  with the Geophysical Statistics Project  at National Center for Atmospheric Research (NCAR), Boulder, Colorado, where he did research on applying Bayesian statistics under the mentorship of Professor Mark Berliner visiting from the Ohio State University. He was a visiting professor at the Hong Kong University of Science and Technology in Kowloon, Hong Kong, China where he taught advanced statistical theory, regression analysis, time series, and Monte Carlo method.  

He was a research scientist doing S-PLUS statistical software development and research (working on individual multidimensional scaling and wavelets time series for turbulence) prior to joining NIST. He has been at NIST as a mathematical statistician since July 2001. His main research activity has been in interdisciplinary statistical research, working side-to-side with subject matter experts to address measurement problems arising from chemical, physical, material sciences, and often involving certifications of measurements of phantoms such as SRMs.  

Ongoing projects of statistical interest:

• Statistics for optical imaging and 3d images
• Functional data analysis for measured spectra or curves
• Bayesian statistics and applications
   

Professional Activities and Societies and Standards Activities:

• Institute of Mathematical Statistics, Lifetime Member
• SIAM:  intermittent 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.

Department of Commerce Bronze medal