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Researcher
- Ryan Dehoff
- Venkatakrishnan Singanallur Vaidyanathan
- Vincent Paquit
- Yong Chae Lim
- Amir K Ziabari
- Andrzej Nycz
- Kuntal De
- Philip Bingham
- Rangasayee Kannan
- Udaya C Kalluri
- Zhili Feng
- Adam Stevens
- Alex Walters
- Biruk A Feyissa
- Brian Post
- Bryan Lim
- Chris Masuo
- Clay Leach
- Debjani Pal
- Diana E Hun
- Gina Accawi
- Gurneesh Jatana
- Jian Chen
- Jiheon Jun
- Mark M Root
- Michael Kirka
- Obaid Rahman
- Peeyush Nandwana
- Philip Boudreaux
- Priyanshi Agrawal
- Roger G Miller
- Sarah Graham
- Sudarsanam Babu
- Tomas Grejtak
- Wei Zhang
- William Peter
- Xiaohan Yang
- Yiyu Wang
- Yukinori Yamamoto

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

A finite element approach integrated with a novel constitute model to predict phase change, residual stresses and part deformation.

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

A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.

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

The technologies provide a coating method to produce corrosion resistant and electrically conductive coating layer on metallic bipolar plates for hydrogen fuel cell and hydrogen electrolyzer applications.

Welding high temperature and/or high strength materials for aerospace or automobile manufacturing is challenging.

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