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Researcher
- Ali Passian
- Singanallur Venkatakrishnan
- Vincent Paquit
- Amir K Ziabari
- Andrzej Nycz
- Diana E Hun
- Kuntal De
- Philip Bingham
- Philip Boudreaux
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- Udaya C Kalluri
- Alex Walters
- Biruk A Feyissa
- Bryan Maldonado Puente
- Chris Masuo
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- Clay Leach
- Corey Cooke
- Debjani Pal
- Gina Accawi
- Gurneesh Jatana
- Harper Jordan
- Joel Asiamah
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- Mark M Root
- Michael Kirka
- Nance Ericson
- Nolan Hayes
- Obaid Rahman
- Peter Wang
- Ryan Kerekes
- Sally Ghanem
- Srikanth Yoginath
- Varisara Tansakul
- Xiaohan Yang

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

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

We present the design, assembly and demonstration of functionality for a new custom integrated robotics-based automated soil sampling technology as part of a larger vision for future edge computing- and AI- enabled bioenergy field monitoring and management technologies called

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

Due to a genes unique nucleotide sequences acquired through horizontal gene transfer, the gene has a transcriptional repressor activity and innate enzymatic role.

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).