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Researcher
- Ryan Dehoff
- Singanallur Venkatakrishnan
- Vincent Paquit
- Amir K Ziabari
- Diana E Hun
- Michael Kirka
- Philip Bingham
- Philip Boudreaux
- Stephen M Killough
- Adam Stevens
- Ahmed Hassen
- Alexander I Kolesnikov
- Alexei P Sokolov
- Alex Plotkowski
- Alice Perrin
- Amit Shyam
- Andres Marquez Rossy
- Bekki Mills
- Blane Fillingim
- Brian Post
- Bryan Maldonado Puente
- Christopher Ledford
- Clay Leach
- Corey Cooke
- David Nuttall
- Gina Accawi
- Gurneesh Jatana
- James Haley
- John Wenzel
- Keju An
- Mark Loguillo
- Mark M Root
- Matthew B Stone
- Nolan Hayes
- Obaid Rahman
- Patxi Fernandez-Zelaia
- Peeyush Nandwana
- Peter Wang
- Rangasayee Kannan
- Roger G Miller
- Ryan Kerekes
- Sally Ghanem
- Sarah Graham
- Shannon M Mahurin
- Sudarsanam Babu
- Tao Hong
- Tomonori Saito
- Victor Fanelli
- Vipin Kumar
- Vlastimil Kunc
- William Peter
- Yan-Ru Lin
- Ying Yang
- Yukinori Yamamoto

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.

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.

High strength, oxidation resistant refractory alloys are difficult to fabricate for commercial use in extreme environments.

This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.

In manufacturing parts for industry using traditional molds and dies, about 70 percent to 80 percent of the time it takes to create a part is a result of a relatively slow cooling process.