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Researcher
- Ali Passian
- Hsuan-Hao Lu
- Joseph Lukens
- Nicholas Peters
- Alex Plotkowski
- Amit Shyam
- Anees Alnajjar
- Jaswinder Sharma
- Joseph Chapman
- Muneer Alshowkan
- Peeyush Nandwana
- Srikanth Yoginath
- Alexey Serov
- Beth L Armstrong
- Blane Fillingim
- Brian Post
- Costas Tsouris
- Daniel Jacobson
- Georgios Polyzos
- Gs Jung
- Gyoung Gug Jang
- James A Haynes
- James J Nutaro
- Lauren Heinrich
- Nageswara Rao
- Pratishtha Shukla
- Radu Custelcean
- Sergiy Kalnaus
- Sudarsanam Babu
- Sudip Seal
- Sumit Bahl
- Thomas Feldhausen
- Xiang Lyu
- Yousub Lee
- Aaron Werth
- Adam Siekmann
- Alexander I Wiechert
- Alex Miloshevsky
- Alice Perrin
- Amit K Naskar
- Amy Moore
- Andres Marquez Rossy
- Brandon Miller
- Brian Williams
- Claire Marvinney
- Craig A Bridges
- Debangshu Mukherjee
- Emilio Piesciorovsky
- Femi Omitaomu
- Gabriel Veith
- Gary Hahn
- Gerry Knapp
- Haowen Xu
- Harper Jordan
- Holly Humphrey
- James Szybist
- Joel Asiamah
- Joel Dawson
- Jonathan Willocks
- Jong K Keum
- Josh Michener
- Jovid Rakhmonov
- Junbin Choi
- Khryslyn G Araño
- Liangyu Qian
- Logan Kearney
- Mariam Kiran
- Marm Dixit
- Md Inzamam Ul Haque
- Meghan Lamm
- Michael Toomey
- Michelle Lehmann
- Mina Yoon
- Nance Ericson
- Nancy Dudney
- Nicholas Richter
- Nihal Kanbargi
- Olga S Ovchinnikova
- Ramanan Sankaran
- Raymond Borges Hink
- Ritu Sahore
- Ryan Dehoff
- Serena Chen
- Sheng Dai
- Sunyong Kwon
- Todd Toops
- Varisara Tansakul
- Vimal Ramanuj
- Vivek Sujan
- Wenjun Ge
- Ying Yang

The eDICEML digital twin is proposed which emulates networks and hosts of an instrument-computing ecosystem. It runs natively on an ecosystem’s host or as a portable virtual machine.

Mechanism-Based Trait Inference in Plants Using Multiplex Networks, AI Agents, and Translation Tools
This system enables the modular design and optimization of complex plant traits by organizing genes and regulatory mechanisms into interpretable clades.

Mechanism-Based Biological Inference via Multiplex Networks, AI Agents and Cross-Species Translation
This invention provides a platform that uses AI agents and biological networks to uncover and interpret disease-relevant biological mechanisms.

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

We tested 48 diverse homologs of SfaB and identified several enzyme variants that were more active than SfaB at synthesizing the nylon-6,6 monomer.

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.

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

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.

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