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Debra L. Kaiser (Fed)

Senior Advisor

Current Research Interests

  • Digital forensics
  • Publication of data
  • NIST Standard Reference Data
  • Documentary standards for nanotechnology
  • Standards for use by regulatory agencies
  • Nanotechnology for cancer applications
  • Measurements and standards for the recovery, reuse, remanufacturing, and recycling of materials

Prior Research Experience

  • Nanotechnology environmental, health, and safety issues
  • Nano-scale Standard Reference Materials
  • Ferroelectric thin film growth and structural and optical property determination
  • Single crystal growth and detwinning of the high temperature superconductor Y-Ba-Cu-O
  • Structure and property determination of high temperature superconducting materials
  • Growth and structural and electrical characterization of II-VI semi-magnetic semiconducting materials

Significant leadership positions

  • Chair, ASTM International Committee E56 on Nanotechnology, 2018-present and 2010-2015
  • Vice-Chair, ASTM International Committee E56 on Nanotechnology, 2016-2017
  • Technical Program Director, NIST Nanotechnology Environment, Health, and Safety Program, 2009-2016
  • Chief, NIST Ceramics Division, 2003-2011
  • Materials Research Society, Meeting Co-Chair, Spring 2007
  • American Association for Crystal Growth, President: 2002–2005




  • ASTM International Award of Appreciation, 2016
  • Department of Commerce Silver Medal for Scientific/Engineering Achievement, 2008
  • Sigma Xi Young Scientist Award for Excellence in Scientific Research, NIST Chapter, 1995
  • American Association for Crystal Growth Young Author Award, 1993
  • United States Department of Commerce, Bronze Medal Award for Scientific/Engineering Achievement, 1990
  • IBM Outstanding Technical Achievement Award, 1989
  • Induction to Phi Beta Kappa, Tau Beta Pi, Sigma Xi, Washington Academy of Sciences


NIST Research Data Framework (RDaF): Version 1.5

Robert Hanisch, Debra L. Kaiser, Alda Yuan, Andrea Medina-Smith, Bonnie C Carroll, Eva Campo
The NIST Research Data Framework (RDaF) is a multifaceted and customizable tool that aims to help shape the future of open data access and research data

An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models

Jason Hattrick-Simpers, Brian DeCost, Aaron Gilad Kusne, Howard Joress, Winnie Wong-Ng, Debra Kaiser, Andriy Zakutayev, Caleb Phillips, Tonio Buonassisi, Shijing Sun, Janak Thapa
Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models
Created October 9, 2019, Updated December 8, 2022