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Researcher
- Singanallur Venkatakrishnan
- Amir K Ziabari
- Diana E Hun
- Philip Bingham
- Philip Boudreaux
- Ryan Dehoff
- Stephen M Killough
- Vincent Paquit
- Aaron Werth
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- Bryan Maldonado Puente
- Corey Cooke
- Emilio Piesciorovsky
- Eve Tsybina
- Gary Hahn
- Gina Accawi
- Gurneesh Jatana
- Harper Jordan
- Jason Jarnagin
- Joel Asiamah
- Joel Dawson
- Mark M Root
- Mark Provo II
- Michael Kirka
- Nance Ericson
- Nolan Hayes
- Obaid Rahman
- Peter Wang
- Raymond Borges Hink
- Rob Root
- Ryan Kerekes
- Sally Ghanem
- Srikanth Yoginath
- Varisara Tansakul
- Viswadeep Lebakula
- Yarom Polsky

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

The ever-changing cellular communication landscape makes it difficult to identify, map, and localize commercial and private cellular base stations (PCBS).

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

Water heaters and heating, ventilation, and air conditioning (HVAC) systems collectively consume about 58% of home energy use.

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

Electrical utility substations are wired with intelligent electronic devices (IEDs), such as protective relays, power meters, and communication switches.

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).

Technologies for optimizing prefab retrofit panel installation using a real-time evaluator is described.