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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
- Gerald Tuskan
- Gina Accawi
- Gurneesh Jatana
- Ilenne Del Valle Kessra
- James Klett
- Keith Carver
- Mark M Root
- Nolan Hayes
- Obaid Rahman
- Patxi Fernandez-Zelaia
- Paul Abraham
- Peter Wang
- Richard Howard
- Roger G Miller
- Ryan Kerekes
- Sally Ghanem
- Sarah Graham
- Steve Bullock
- Sudarsanam Babu
- Thomas Butcher
- Trevor Aguirre
- Vilmos Kertesz
- William Peter
- Xiaohan Yang
- Yan-Ru Lin
- Yang Liu
- 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.

Detection of gene expression in plants is critical for understanding the molecular basis of plant physiology and plant responses to drought, stress, climate change, microbes, insects and other factors.
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.