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Researcher
- Ali Passian
- Venkatakrishnan Singanallur Vaidyanathan
- Amir K Ziabari
- Diana E Hun
- Philip Bingham
- Philip Boudreaux
- Ryan Dehoff
- Stephen M Killough
- Vincent Paquit
- Annetta Burger
- Bryan Maldonado Puente
- Carter Christopher
- Chance C Brown
- Claire Marvinney
- Corey Cooke
- Debraj De
- Gautam Malviya Thakur
- Gina Accawi
- Gurneesh Jatana
- Harper Jordan
- James Gaboardi
- Jesse McGaha
- Joel Asiamah
- Joel Dawson
- Kevin Sparks
- Liz McBride
- Mark M Root
- Michael Kirka
- Nance Ericson
- Nolan Hayes
- Obaid Rahman
- Peter Wang
- Ryan Kerekes
- Sally Ghanem
- Srikanth Yoginath
- Todd Thomas
- Varisara Tansakul
- Xiuling Nie

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.

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

Technologies directed quantum spectroscopy and imaging with Raman and surface-enhanced Raman scattering 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).