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Media Contacts
![ORNL researchers are establishing a digital thread of data, algorithms and workflows to produce a continuously updated model of earth systems.](/sites/default/files/styles/list_page_thumbnail/public/2023-11/MicrosoftTeams-image%20%2823%29_0.png?h=c6980913&itok=cK99Pg3y)
Digital twins are exactly what they sound like: virtual models of physical reality that continuously update to reflect changes in the real world.
![Cody Lloyd stands in front of images of historical nuclear field testing. The green and red dots are the machine learning algorithm recognizing features in the image. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-08/2023-P05797_0.jpg?h=4a7d1ed4&itok=S8h_wvJX)
Cody Lloyd became a nuclear engineer because of his interest in the Manhattan Project, the United States’ mission to advance nuclear science to end World War II. As a research associate in nuclear forensics at ORNL, Lloyd now teaches computers to interpret data from imagery of nuclear weapons tests from the 1950s and early 1960s, bringing his childhood fascination into his career
![Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-08/2023-P06111_0.jpg?h=c6980913&itok=dgI-yVRh)
After completing a bachelor’s degree in biology, Toya Beiswenger didn’t intend to go into forensics. But almost two decades later, the nuclear security scientist at ORNL has found a way to appreciate the art of nuclear forensics.
![Two researchers standing back to back in a grassy area](/sites/default/files/styles/list_page_thumbnail/public/2023-07/CSJ_1716_updated.jpg?h=2dfa0735&itok=U-3yNm3M)
When geoinformatics engineering researchers at the Department of Energy’s Oak Ridge National Laboratory wanted to better understand changes in land areas and points of interest around the world, they turned to the locals — their data, at least.
![Tristen Mullins. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-06/mullins_0.jpg?h=dab30fcb&itok=dsFGJyMz)
Tristen Mullins enjoys the hidden side of computers. As a signals processing engineer for ORNL, she tries to uncover information hidden in components used on the nation’s power grid — information that may be susceptible to cyberattacks.
![Philipe Ambrozio Dias. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-11/2022-P09862.jpg?h=4a7d1ed4&itok=CblZ5Rj4)
Having lived on three continents spanning the world’s four hemispheres, Philipe Ambrozio Dias understands the difficulties of moving to a new place.
![Thomaz Carvalhaes. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-09/2022-P06032_0.jpg?h=252f27fa&itok=A2HrJ-y6)
In human security research, Thomaz Carvalhaes says, there are typically two perspectives: technocentric and human centric. Rather than pick just one for his work, Carvalhaes uses data from both perspectives to understand how technology impacts the lives of people.
![Data from different sources are joined on platforms created by ORNL researchers to offer better information for decision makers. Credit: ORNL/Nathan Armistead](/sites/default/files/styles/list_page_thumbnail/public/2022-07/COVID%20dashboards%20story%20graphic_0.jpg?h=d1cb525d&itok=ubNOO2W4)
When the COVID-19 pandemic stunned the world in 2020, researchers at ORNL wondered how they could extend their support and help
![LandScan Global depicts population distribution estimates across the planet. The darker orange and red colors above indicate higher population density. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-05/Picture1_0.jpg?h=9d172ced&itok=uYwYp-pW)
It’s a simple premise: To truly improve the health, safety, and security of human beings, you must first understand where those individuals are.
![ORNL, VA and Harvard researchers developed a sparse matrix full of anonymized information on what is thought to be the largest cohort of healthcare data used for this type of research in the U.S. The matrix can be probed with different methods, such as KESER, to gain new insights into human health. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-04/2022-G00330_KESER%20Illustration_0.jpg?h=1cb48fc4&itok=c6ZuDdDg)
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.