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Researcher
- Michael Kirka
- Ryan Dehoff
- Rangasayee Kannan
- Singanallur Venkatakrishnan
- Adam Stevens
- Amir K Ziabari
- Christopher Ledford
- Diana E Hun
- Peeyush Nandwana
- Philip Bingham
- Philip Boudreaux
- Stephen M Killough
- Vincent Paquit
- Alice Perrin
- Beth L Armstrong
- Brian Post
- Bryan Maldonado Puente
- Corey Cooke
- Corson Cramer
- Fred List III
- Gina Accawi
- Gurneesh Jatana
- Ian Greenquist
- James Klett
- Keith Carver
- Mark M Root
- Nolan Hayes
- Obaid Rahman
- Patxi Fernandez-Zelaia
- Peter Wang
- Richard Howard
- Roger G Miller
- Ryan Kerekes
- Sally Ghanem
- Sarah Graham
- Steve Bullock
- Sudarsanam Babu
- Thomas Butcher
- Trevor Aguirre
- 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.
Red mud residue is an industrial waste product generated during the processing of bauxite ore to extract alumina for the steelmaking industry. Red mud is rich in minerals in bauxite like iron and aluminum oxide, but also heavy metals, including arsenic and mercury.

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.

This technology aims to provide and integrated and oxidation resistant cladding or coating onto carbon-based composites in seconds.