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Researcher
- Alex Plotkowski
- Amit Shyam
- Ryan Dehoff
- Singanallur Venkatakrishnan
- Amir K Ziabari
- Diana E Hun
- James A Haynes
- Mingyan Li
- Philip Bingham
- Philip Boudreaux
- Sam Hollifield
- Stephen M Killough
- Sumit Bahl
- Vincent Paquit
- Alice Perrin
- Andres Marquez Rossy
- Brian Weber
- Bryan Maldonado Puente
- Corey Cooke
- Gerry Knapp
- Gina Accawi
- Gurneesh Jatana
- Isaac Sikkema
- Joseph Olatt
- Jovid Rakhmonov
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- Kunal Mondal
- Lilian V Swann
- Luke Koch
- Mahim Mathur
- Mark M Root
- Mary A Adkisson
- Michael Kirka
- Nicholas Richter
- Nolan Hayes
- Obaid Rahman
- Oscar Martinez
- Peeyush Nandwana
- Peter Wang
- Ryan Kerekes
- Sally Ghanem
- Sunyong Kwon
- T Oesch
- Ying Yang

ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.

Currently available cast Al alloys are not suitable for various high-performance conductor applications, such as rotor, inverter, windings, busbar, heat exchangers/sinks, etc.

The invented alloys are a new family of Al-Mg alloys. This new family of Al-based alloys demonstrate an excellent ductility (10 ± 2 % elongation) despite the high content of impurities commonly observed in recycled aluminum.

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

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

Real-time tracking and monitoring of radioactive/nuclear materials during transportation is a critical need to ensure safety and security. Current technologies rely on simple tagging, using sensors attached to transport containers, but they have limitations.

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).