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Researcher
- Singanallur Venkatakrishnan
- Amir K Ziabari
- Andrzej Nycz
- Chris Masuo
- Diana E Hun
- Luke Meyer
- Peter Wang
- Philip Bingham
- Philip Boudreaux
- Ryan Dehoff
- Stephen M Killough
- Vincent Paquit
- Vlastimil Kunc
- William Carter
- Ahmed Hassen
- Alexander I Kolesnikov
- Alexei P Sokolov
- Alex Walters
- Bekki Mills
- Bruce Hannan
- Bryan Maldonado Puente
- Corey Cooke
- Dan Coughlin
- Dave Willis
- Gina Accawi
- Gurneesh Jatana
- Jim Tobin
- John Wenzel
- Josh Crabtree
- Joshua Vaughan
- Keju An
- Kim Sitzlar
- Loren L Funk
- Luke Chapman
- Mark Loguillo
- Mark M Root
- Matthew B Stone
- Merlin Theodore
- Michael Kirka
- Nolan Hayes
- Obaid Rahman
- Polad Shikhaliev
- Ryan Kerekes
- Sally Ghanem
- Shannon M Mahurin
- Steven Guzorek
- Subhabrata Saha
- Sydney Murray III
- Tao Hong
- Theodore Visscher
- Tomonori Saito
- Vasilis Tzoganis
- Vasiliy Morozov
- Victor Fanelli
- Vipin Kumar
- Vladislav N Sedov
- Yacouba Diawara
- Yun Liu

ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.

We presented a novel apparatus and method for laser beam position detection and pointing stabilization using analog position-sensitive diodes (PSDs).

We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.

ORNL has developed a large area thermal neutron detector based on 6LiF/ZnS(Ag) scintillator coupled with wavelength shifting fibers. The detector uses resistive charge divider-based position encoding.

Neutron scattering experiments cover a large temperature range in which experimenters want to test their samples.

Neutron beams are used around the world to study materials for various purposes.

Through the use of splicing methods, joining two different fiber types in the tow stage of the process enables great benefits to the strength of the material change.