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Researcher
- Joseph Chapman
- Nicholas Peters
- Yong Chae Lim
- Zhili Feng
- Hsuan-Hao Lu
- Jian Chen
- Joseph Lukens
- Mike Zach
- Muneer Alshowkan
- Rangasayee Kannan
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- Anees Alnajjar
- Annetta Burger
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- Debjani Pal
- Debraj De
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- Jiheon Jun
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- Kevin Sparks
- Kuntal De
- Laetitia H Delmau
- Liz McBride
- Luke Sadergaski
- Mariam Kiran
- Nedim Cinbiz
- Padhraic L Mulligan
- Peeyush Nandwana
- Priyanshi Agrawal
- Roger G Miller
- Ryan Dehoff
- Sandra Davern
- Sarah Graham
- Sudarsanam Babu
- Todd Thomas
- Tomas Grejtak
- Tony Beard
- William Peter
- Xiuling Nie
- Yiyu Wang
- Yukinori Yamamoto

Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.

Here we present a solution for practically demonstrating path-aware routing and visualizing a self-driving network.

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

Technologies directed to polarization agnostic continuous variable quantum key distribution are described.
Contact:
To learn more about this technology, email partnerships@ornl.gov or call 865-574-1051.

Ruthenium is recovered from used nuclear fuel in an oxidizing environment by depositing the volatile RuO4 species onto a polymeric substrate.

The development of quantum networking requires architectures capable of dynamically reconfigurable entanglement distribution to meet diverse user needs and ensure tolerance against transmission disruptions.

Polarization drift in quantum networks is a major issue. Fiber transforms a transmitted signal’s polarization differently depending on its environment.

This invention addresses a key challenge in quantum communication networks by developing a controlled-NOT (CNOT) gate that operates between two degrees of freedom (DoFs) within a single photon: polarization and frequency.

This invention is directed to a machine leaning methodology to quantify the association of a set of input variables to a set of output variables, specifically for the one-to-many scenarios in which the output exhibits a range of variations under the same replicated input condi

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.