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Researcher
- Vivek Sujan
- Adam Siekmann
- Omer Onar
- Sam Hollifield
- Subho Mukherjee
- Venkatakrishnan Singanallur Vaidyanathan
- Amir K Ziabari
- Chad Steed
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- Mingyan Li
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- Luke Koch
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- Mark Provo II
- Mary A Adkisson
- Michael Kirka
- Nance Ericson
- Nolan Hayes
- Obaid Rahman
- Oscar Martinez
- Peter Wang
- Raymond Borges Hink
- Rob Root
- Ryan Kerekes
- Sally Ghanem
- Samudra Dasgupta
- Shajjad Chowdhury
- Srikanth Yoginath
- T Oesch
- Varisara Tansakul
- 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).

The growing demand for electric vehicles (EVs) has necessitated significant advancements in EV charging technologies to ensure efficient and reliable operation.

The growing demand for renewable energy sources has propelled the development of advanced power conversion systems, particularly in applications involving fuel cells.

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.

This invention presents a multiport converter (MPC) based power supply to charge the 12 V and 24 V auxiliary batteries in heavy duty (HD) fuel cell (FC) electric vehicle (EV) power train.

This invention presents an integrated strategy to reduce end-user electricity costs and grid carbon emissions by efficiently utilizing Distributed Energy Resources (DER) and grid-scale electrical energy storage systems, such as batteries.