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N. Alan Heckert (Fed)

Alan Heckert joined SED in 1996. He came to NIST in 1985 as member of the consulting group for the NIST supercomputer center. He previously worked for 4½ years for the Statistical Research Division of the Census Bureau. His primary areas of interest are statistical computing, statistical graphics and distributional modeling.

Alan is currently the lead developer for the e-FITS and e-Metrology web projects. Current collaborators include Gale Holmes, Jae-Hyun Kim, and Walter McDonough of the Polymers and Complex Fluids group and Jeffrey Fong of the Mathematical Analysis and Modeling group. Some past projects include computing support for various radiation detection test campaigns conducted by the Department of Homeland Security and for an MEL scatterfield microscopy project.

Current Projects/Duties:

Technical Areas of Research and Consulting:

  • Statistical graphics
  • Statistical computing

Professional Activities and Societies and Standards Activities

Awards:

  • Department of Commerce (DoC) Silver Medal, 2003

Publications

Characterization of Reference Materials 8690 to 8693

Author(s)
Jessica Reiner, Benjamin Place, N. Alan Heckert, Katherine Peter, Alix Rodowa
The National Institute of Standards and Technology (NIST) Reference Materials (RMs) 8690 Per- and Polyfluoroalkyl Substances (PFAS) in Aqueous Film-Forming

2022 NCWM-NIST National Survey on 20 lb LPG (Propane) Cylinders

Author(s)
David Sefcik, Katrice Lippa, N. Alan Heckert, Stephen Benjamin, Ivan Hankins, Don Onwiler
The National Conference on Weights and Measures (NCWM) in partnership and cooperation with the NIST Office of Weights and Measures (OWM) and the Weights and

Interlaboratory Attribute Analytics Metrics from the MAM Consortium Round Robin Study

Author(s)
Trina Mouchahoir, John E. Schiel, Rich Rogers, N. Alan Heckert, Benjamin Place, Aaron Ammerman, Xiaoxiao Li, Tom Robinson, Brian Schmidt, Chris M. Chumsae, Xinbi Li, Anton V. Manuilov, Bo Yan, Gregory O. Staples, Da Ren, Alexander J. Veach, Dongdong Wang, Wael Yared, Zoran Sosic, Yan Wang, Li Zang, Anthony M. Leone, Peiran Liu, Richard Ludwig, Li Tao, Wei Wu, Ahmet Cansizoglu, Andrew Hanneman, Greg W. Adams, Irina Perdivara, Hunter Walker, Margo Wilson, Arnd Brandenburg, Nick DeGraan-Weber, Stefano Gotta, Joe Shambaugh, Melissa Alvarez, X. Christopher Yu, Li Cao, Chun Shao, Andrew Mahan, Hirsh Nanda, Kristen Nields, Nancy Nightlinger, Ben Niu, Jihong Wang, Wei Xu, Gabriella Leo, Nunzio Sepe, Yan-Hui Liu, Bhumit A. Patel, Douglas Richardson, Yi Wang, Daniela Tizabi, Oleg V. Borisov, Yali Lu, Ernest L. Maynard, Albrecht Gruhler, Kim F. Haselmann, Thomas N. Krogh, Carsten P. Sönksen, Simon Letarte, Sean Shen, Kristin Boggio, Keith Johnson, Wenqin Ni, Himakshi Patel, David Ripley, Jason C. Rouse, Ying Zhang, Carly Daniels, Andrew Dawdy, Olga Friese, Thomas W. Powers, Justin B. Sperry, Josh Woods, Eric Carlson, K. Ilker Sen, St John Skilton, Michelle Busch, Anders Lund, Martha Stapels, Xu Guo, Sibylle Heidelberger, Harini Kaluarachchi, Sean McCarthy, John Kim, Jing Zhen, Ying Zhou, Sarah Rogstad, Xiaoshi Wang, Jing Fang, Weibin Chen, Ying Qing Yu, John G. Hoogerheide, Rebecca Scott, Hua Yuan
The multi-attribute method (MAM) was conceived as a single assay to potentially replace multiple single-attribute assays that have long been used in process
Created October 9, 2019, Updated December 8, 2022