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Researcher
- Isabelle Snyder
- Venkatakrishnan Singanallur Vaidyanathan
- Adam Siekmann
- Amir K Ziabari
- Emilio Piesciorovsky
- Philip Bingham
- Ryan Dehoff
- Subho Mukherjee
- Vincent Paquit
- Vivek Sujan
- Aaron Werth
- Aaron Wilson
- Alexander I Kolesnikov
- Alexei P Sokolov
- Ali Riza Ekti
- Bekki Mills
- Diana E Hun
- Elizabeth Piersall
- Eve Tsybina
- Gary Hahn
- Gina Accawi
- Gurneesh Jatana
- John Wenzel
- Keju An
- Mark Loguillo
- Mark M Root
- Matthew B Stone
- Michael Kirka
- Nils Stenvig
- Obaid Rahman
- Ozgur Alaca
- Philip Boudreaux
- Raymond Borges Hink
- Shannon M Mahurin
- Tao Hong
- Tomonori Saito
- Victor Fanelli
- Viswadeep Lebakula
- Yarom Polsky

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

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

Faults in the power grid cause many problems that can result in catastrophic failures. Real-time fault detection in the power grid system is crucial to sustain the power systems' reliability, stability, and quality.

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

Water heaters and heating, ventilation, and air conditioning (HVAC) systems collectively consume about 58% of home energy use.

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

This disclosure introduces an innovative tool that capitalizes on historical data concerning the carbon intensity of the grid, distinct to each electric zone.

This disclosure introduces an innovative tool that capitalizes on historical data concerning the carbon intensity of the grid, distinct to each electric zone.