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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
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- Amit Shyam
- Andres Marquez Rossy
- Blane Fillingim
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- Christopher Ledford
- Clay Leach
- Corey Cooke
- David Nuttall
- Fred List III
- Gina Accawi
- Gurneesh Jatana
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- Obaid Rahman
- Patxi Fernandez-Zelaia
- Peeyush Nandwana
- Peter Wang
- Rangasayee Kannan
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- Roger G Miller
- Ryan Kerekes
- Sally Ghanem
- Sarah Graham
- Sudarsanam Babu
- Thomas Butcher
- 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.

A pressure burst feature has been designed and demonstrated for relieving potentially hazardous excess pressure within irradiation capsules used in the ORNL High Flux Isotope Reactor (HFIR).

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

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.

Current technology for heating, ventilation, and air conditioning (HVAC) and other uses such as vending machines rely on refrigerants that have high global warming potential (GWP).

This technology combines 3D printing and compression molding to produce high-strength, low-porosity composite articles.