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Researcher
- Alex Plotkowski
- Amit Shyam
- Ryan Dehoff
- Sam Hollifield
- Venkatakrishnan Singanallur Vaidyanathan
- Amir K Ziabari
- Chad Steed
- Diana E Hun
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- Andres Marquez Rossy
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- Mark Provo II
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- Nance Ericson
- Nicholas Richter
- Nolan Hayes
- Obaid Rahman
- Oscar Martinez
- Peeyush Nandwana
- Peter Wang
- Raymond Borges Hink
- Rob Root
- Ryan Kerekes
- Sally Ghanem
- Samudra Dasgupta
- Srikanth Yoginath
- Sunyong Kwon
- T Oesch
- Varisara Tansakul
- Yarom Polsky
- Ying Yang

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

Currently available cast Al alloys are not suitable for various high-performance conductor applications, such as rotor, inverter, windings, busbar, heat exchangers/sinks, etc.

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

The invented alloys are a new family of Al-Mg alloys. This new family of Al-based alloys demonstrate an excellent ductility (10 ± 2 % elongation) despite the high content of impurities commonly observed in recycled aluminum.

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

The QVis Quantum Device Circuit Optimization Module gives users the ability to map a circuit to a specific quantum devices based on the device specifications.

QVis is a visual analytics tool that helps uncover temporal and multivariate variations in noise properties of quantum devices.

Modern automobiles are operated by small computers that communicate critical information via a broadcast-based network architecture called controller area network (CAN).

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