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Researcher
- Diana E Hun
- Som Shrestha
- Philip Boudreaux
- Tomonori Saito
- Venkatakrishnan Singanallur Vaidyanathan
- Zoriana Demchuk
- Amir K Ziabari
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- Mahabir Bhandari
- Nolan Hayes
- Philip Bingham
- Ryan Dehoff
- Shiwanka Vidarshi Wanasinghe Wanasinghe Mudiyanselage
- Stephen M Killough
- Venugopal K Varma
- Vincent Paquit
- Achutha Tamraparni
- Adam Aaron
- Andre O Desjarlais
- Catalin Gainaru
- Charles D Ottinger
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- Femi Omitaomu
- Gina Accawi
- Gurneesh Jatana
- Haowen Xu
- Karen Cortes Guzman
- Kuma Sumathipala
- Mark M Root
- Mengjia Tang
- Michael Kirka
- Natasha Ghezawi
- Obaid Rahman
- Peter Wang
- Ryan Kerekes
- Sally Ghanem
- Zhenglai Shen

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.

The incorporation of low embodied carbon building materials in the enclosure is increasing the fuel load for fire, increasing the demand for fire/flame retardants.

We will develop an AI-powered autonomous software development pipeline to help urban scientists develop advanced research software (e.g., digital twins and cyberinfrastructure) to support smart city research and management without the need to write codes or know software engin

The traditional window installation process involves many steps. These are becoming even more complex with newer construction requirements such as installation of windows over exterior continuous insulation walls.

Commercial closed-cell insulation foam boards reduce their thermal resistivity by up to 30% due to gas diffusion in and out of foam cells.

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