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Researcher
- Singanallur Venkatakrishnan
- Amir K Ziabari
- Diana E Hun
- Philip Bingham
- Philip Boudreaux
- Ryan Dehoff
- Stephen M Killough
- Vincent Paquit
- Yaosuo Xue
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- Bryan Maldonado Puente
- Corey Cooke
- Debjani Pal
- Fei Wang
- Gina Accawi
- Gurneesh Jatana
- Jeffrey Einkauf
- Jennifer M Pyles
- Kuntal De
- Laetitia H Delmau
- Luke Sadergaski
- Mark M Root
- Michael Kirka
- Nolan Hayes
- Obaid Rahman
- Peter Wang
- Phani Ratna Vanamali Marthi
- Rafal Wojda
- Ryan Kerekes
- Sally Ghanem
- Sreenivasa Jaldanki
- Suman Debnath
- Sunil Subedi
- Yonghao Gui

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

Ruthenium is recovered from used nuclear fuel in an oxidizing environment by depositing the volatile RuO4 species onto a polymeric substrate.

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

Measurements of grid voltage and current are essential for the optimal operation of the grid protection and control (P&C) systems.

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

Multi-terminal DC (MTdc) systems based on high-voltage DC (HVDC) transmission technology is an upcoming concept. In such systems, either asymmetric monopole or bi-pole systems are generally employed. Such systems are not suitable for easy expansion.

Stability performance of interconnected power grids plays crucial roles on their secure operation to prevent cascading failure and blackout.

Technologies directed to a multi-port autonomous reconfigurable solar power plant are described.

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).