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 was fortunate to be exposed to some cutting-edging atmospheric research and met some leading atmospheric scientists and working with Professor Mark Berliner hailing 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 (similar to R) statistical software development and research prior to joining NIST. He has been at NIST as a mathematical statistician since July 2001. His main research activity has been in collaborative and consulting statistical research, working side-to-side with subject matter experts to address measurement problems arising from physical, material sciences, and quantitative medical imaging, and many efforts are involved in developing statistical methodology and computations for certifications of SRMs and NIST measurement services.  

Statistical research interests:

• Statistics for optical imaging and 3d images
• Functional data analysis for measured spectra or curves
• Time series analysis
• Morphology and geometric statistics
   

Professional Activities and Societies and Standards Activities:

• Institute of Mathematical Statistics, Lifetime Member
• SIAM:  intermittent member
• RSNA QIBA Volumetric Committee
• OSAC: forensic imaging and shape analysis

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