Z.Q. John Lu

NIST/ITL
100 Bureau Dr., Stop 8980<
Gaithersburg, MD 20899-8980

Biography:
Z.Q. John Lu is a mathematical statistician in the Information Technology Laboratory at National Institute of Standards and Technology (NIST) where he has been for over five years. Prior to that he was a research scientist at Insightful Corporation in Seattle, a visiting assistant professor at Hong Kong University of Science and Technology, Hong Kong, China. He was also an early member of the Geophysical Statistical Project at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado. He has done significant work on in the areas of nonparametric regression including multivariate locally weighted regression and development of a singular design model for differential manifold data, nonlinear time series and chaos, Bayesian methods for data assimilation and adaptive weather observations. His main focus of recent work is in the area of high-dimensional data analysis, such as statistical analysis of mass distribution data, microarray gene expression metrology, and statistical methodology for 3-d topography using the Geometry Measuring Machine (GEMM) which is partly funded by NIST’s Advanced Technology Program. He has a PhD and M.S. in statistics from the University of North Carolina, Chapel Hill, and B.S. in mathematics from Peking University (Beida), Beijing, China..

Talk Title: 3-D Topography Using Curvature-based Geometry Measuring Machine

Abstract: Contemporary optical systems have used complex shape optics, which are aspheric. An innovative new approach is required in order to overcome the difficulties with existing techniques using standard interferometer testing for flatness and sphere surfaces. NIST’s Manufacturing Engineering Laboratory has developed the Geometry Measuring Machine (GEMM) based on the curvature measurements using local topography data from a miniature Twyman-Green phase-measuring interferometer. In this talk I will describe for the first time how curvature measurements can be extracted based on 3-d topographic data. I will also describe how curvature data can be used to reconstruct the global surface by using the smoothing spline and reproducing kernel Hilbert space methodology. Statistical methods for testing the comparability of surface measurements by two measurement methods will also be discussed. (This presentation is based on collaboration with Nadia Machkour-Deshayes, CNRS, France, Charles Hagwood (NIST/ITL), Johannes Soons (NIST/MEL))