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Media Contacts
![Director of ORNL’s AI Initiative Prasanna Balaprakash addresses attendees at the Generative AI for ORNL Science Workshop. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-09/prasannaSMC2023_0.jpg?h=89f9a9b4&itok=N5nInOPo)
The Department of Energy’s Oak Ridge National Laboratory hosted its Smoky Mountains Computational Science and Engineering Conference for the first time in person since the COVID pandemic broke in 2020. The conference, which celebrated its 20th consecutive year, took place at the Crowne Plaza Hotel in downtown Knoxville, Tenn., in late August.
![Tom Karnowski (left) and Jordan Johnson (right). Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-09/2018-P06415%20and%202022-P10212_0.jpg?h=0cfd17d8&itok=E-Rqrcrx)
Tom Karnowski and Jordan Johnson of ORNL have been named chair and vice chair, respectively, of the East Tennessee section of the Institute of Electrical and Electronics Engineers, or IEEE.
![Mike Huettel](/sites/default/files/styles/list_page_thumbnail/public/2023-08/2023-P00819.jpg?h=4a7d1ed4&itok=SHi9F_hH)
Mike Huettel is a cyber technical professional. He also recently completed the 6-month Cyber Warfare Technician course for the United States Army, where he learned technical and tactical proficiency leadership in operations throughout the cyber domain.
![JungHyun Bae portrait](/sites/default/files/styles/list_page_thumbnail/public/2023-06/2022-P11643_1.jpg?h=599acf13&itok=6eDQDGzr)
JungHyun Bae is a nuclear scientist studying applications of particles that have some beneficial properties: They are everywhere, they are unlimited, they are safe.
![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.
![Stephen Dahunsi. Credit: Jason Richards/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2023-03/2018-P00115_Stephen%20Dahunsi.jpg?h=b6236d98&itok=lRQh92bt)
Stephen Dahunsi’s desire to see more countries safely deploy nuclear energy is personal. Growing up in Nigeria, he routinely witnessed prolonged electricity blackouts as a result of unreliable energy supplies. It’s a problem he hopes future generations won’t have to experience.
![ORNL identity science researcher Nell Barber works on a facial recognition camera. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-07/Picture1.jpg?h=47322e82&itok=CTajZTiF)
Though Nell Barber wasn’t sure what her future held after graduating with a bachelor’s degree in psychology, she now uses her interest in human behavior to design systems that leverage machine learning algorithms to identify faces in a crowd.
![Logan Sturm, Alvin M. Weinberg Fellow at ORNL, creates a mashup between additive manufacturing and cybersecurity research. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-05/sturm-lab.jpg?h=1de2f7a8&itok=nYiuVTGx)
How an Alvin M. Weinberg Fellow is increasing security for critical infrastructure components
![The ORNL researchers’ findings may enable better detection of uranium tetrafluoride hydrate, a little-studied byproduct of the nuclear fuel cycle, and better understanding of how environmental conditions influence the chemical behavior of fuel cycle materials. Credit: Kevin Pastoor/Colorado School of Mines](/sites/default/files/styles/list_page_thumbnail/public/2022-05/UF4%20hydrate.png?h=d318f057&itok=spT-Dg48)
ORNL researchers used the nation’s fastest supercomputer to map the molecular vibrations of an important but little-studied uranium compound produced during the nuclear fuel cycle for results that could lead to a cleaner, safer world.
![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.