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Researcher
- Ryan Dehoff
- Venkatakrishnan Singanallur Vaidyanathan
- Vincent Paquit
- Amir K Ziabari
- Diana E Hun
- Michael Kirka
- Philip Bingham
- Philip Boudreaux
- Stephen M Killough
- Adam Stevens
- Ahmed Hassen
- Alex Plotkowski
- Alice Perrin
- Amit Shyam
- Andres Marquez Rossy
- Annetta Burger
- Blane Fillingim
- Brian Post
- Bryan Maldonado Puente
- Carter Christopher
- Chance C Brown
- Christopher Ledford
- Clay Leach
- Corey Cooke
- David Nuttall
- Debraj De
- Gautam Malviya Thakur
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- Gurneesh Jatana
- James Gaboardi
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- Kevin Sparks
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- Mark M Root
- Nolan Hayes
- Obaid Rahman
- Patxi Fernandez-Zelaia
- Peeyush Nandwana
- Peter Wang
- Rangasayee Kannan
- Roger G Miller
- Ryan Kerekes
- Sally Ghanem
- Sarah Graham
- Sudarsanam Babu
- Todd Thomas
- Vipin Kumar
- Vlastimil Kunc
- William Peter
- Xiuling Nie
- 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.

Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.

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.