The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Michael Gaitan sits on this spectrum.
This scientist: 179 publications — 39th percentile
39% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 804 publications or more.
The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Michael Gaitan sits on this spectrum.
This scientist: 43 D-Index — 39th percentile
39% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 107 D-Index or more.
Michael Gaitan is a researcher affiliated with the National Institute of Standards and Technology in the United States. Their work concentrates primarily on the fields of Engineering and Decision Sciences, with a focus on several specialized subfields including Mechanical Engineering, Statistics, Probability and Uncertainty, Ocean Engineering, Aerospace Engineering, and Electrical and Electronic Engineering.
Their research topics notably cover advanced measurement and metrology techniques, scientific measurement and uncertainty evaluation, geophysics and sensor technology, inertial sensor and navigation systems, optical and acousto-optic technologies, advanced sensor technologies research, and advanced fiber optic sensors.
Michael Gaitan has published several papers in a range of peer-reviewed journals. Selected recent publications include:
The researcher has also contributed multiple works to specific scientific venues, including:
Collaborations are an integral part of Michael Gaitan's work. Frequent co-authors across their publications include:
The body of work reflects a concentration on measurement science, sensor calibration, and uncertainty analysis in various engineering contexts, particularly involving accelerometers and vibrometer technology. Their contributions span methodologies for reducing calibration uncertainty and validating uncertainty analysis approaches.
David J. Ross;Michael Gaitan;Laurie E. Locascio
Larissa E. Martynova;Laurie E. Locascio;Michael Gaitan;Gary W. Kramer
J.Y.-C. Chang;A.A. Abidi;M. Gaitan
Andreas Jahn;Wyatt N. Vreeland;Michael Gaitan;Laurie E. Locascio
Andreas Jahn;Wyatt N Vreeland;Don L DeVoe;Laurie E Locascio
Andreas Jahn;Andreas Jahn;Samuel M. Stavis;Jennifer S. Hong;Wyatt N. Vreeland
J.S. Suehle;R.E. Cavicchi;M. Gaitan;S. Semancik
Brian J. Polk;Anna Stelzenmuller;Geraldine I. Mijares;William A. MacCrehan
Andreas Jahn;Andreas Jahn;Joseph E. Reiner;Wyatt N. Vreeland;Don L. DeVoe
Susan L. R. Barker;David Ross;Michael J. Tarlov;Michael Gaitan
Jingdong Xu;Laurie Locascio;Michael Gaitan;Cheng S. Lee
Timothy J. Johnson;David Ross;Michael Gaitan;Laurie E. Locascio
K. F. Galloway;M. Gaitan;T. J. Russell
Richard E. Cavicchi;John S. Suehle;Kenneth G. Kreider;Michael Gaitan
R.E. Cavicchi;J.S. Suehle;K.G. Kreider;M. Gaitan
V. Milanovic;M. Ozgur;D.C. DeGroot;J.A. Jargon
Richard Cavicchi;Stephen Semancik;John S. Suehle;Michael Gaitan
Steven C. Witczak;John S. Suehle;Michael Gaitan
V. Milanovic;M. Gaitan;E.D. Bowen;M.E. Zaghloul
Stephen Semancik;Richard E. Cavicchi;Michael Gaitan;John S. Suehle
M. Parameswaran;A.M. Robinson;D.L. Blackburn;M. Gaitan
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