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Media Contacts
![Man in blue shirt and grey pants holds laptop and poses next to a green plant in a lab.](/sites/default/files/styles/list_page_thumbnail/public/2024-06/2024-P09065.jpg?h=036a71b7&itok=szEF_SdO)
John Lagergren, a staff scientist in Oak Ridge National Laboratory’s Plant Systems Biology group, is using his expertise in applied math and machine learning to develop neural networks to quickly analyze the vast amounts of data on plant traits amassed at ORNL’s Advanced Plant Phenotyping Laboratory.
![Frontier supercomputer sets new standard in molecular simulation](/sites/default/files/styles/list_page_thumbnail/public/2024-05/OLCF_LammpsBanner.png?h=ae114f5c&itok=h_Bam9gm)
When scientists pushed the world’s fastest supercomputer to its limits, they found those limits stretched beyond even their biggest expectations. In the latest milestone, a team of engineers and scientists used Frontier to simulate a system of nearly half a trillion atoms — the largest system ever modeled and more than 400 times the size of the closest competition.
![Jiafu Mao, left, and Yaoping Wang discuss their analysis of urban and rural vegetation resilience across the United States in the EVEREST visualization lab at ORNL. Credit: Carlos Jones, ORNL/U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2024-05/2024-P07278R.jpg?h=c6980913&itok=lVwfnfEq)
Scientists at ORNL completed a study of how well vegetation survived extreme heat events in both urban and rural communities across the country in recent years. The analysis informs pathways for climate mitigation, including ways to reduce the effect of urban heat islands.
![Quietly making noise: Measuring differential privacy could balance meaningful analytics and identity protection](/sites/default/files/styles/list_page_thumbnail/public/2024-04/AdobeStock_599537692.jpeg?h=8059516b&itok=oDcA1WvL)
To balance personal safety and research innovation, researchers at ORNL are employing a mathematical technique known as differential privacy to provide data privacy guarantees.
Simulations performed on the Summit supercomputer at ORNL are cutting through that time and expense by helping researchers digitally customize the ideal alloy.
Integral to the functionality of ORNL's Frontier supercomputer is its ability to store the vast amounts of data it produces onto its file system, Orion. But even more important to the computational scientists running simulations on Frontier is their capability to quickly write and read to Orion along with effectively analyzing all that data. And that’s where ADIOS comes in.
![ORNL’s Suhas Sreehari explains the algebraic and topological foundations of representation systems, used in generative AI technology such as large language models. Credit: Lena Shoemaker/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2024-04/powered%20by%20match.jpg?h=384e2afb&itok=wJegcDZm)
In the age of easy access to generative AI software, user can take steps to stay safe. Suhas Sreehari, an applied mathematician, identifies misconceptions of generative AI that could lead to unintentionally bad outcomes for a user.
![ORNL’s Kate Evans has been awarded the 2024 Society for Industrial and Applied Mathematicians Activity Group on Mathematics of Planet Earth Prize.](/sites/default/files/styles/list_page_thumbnail/public/2024-03/Evans_SIAM.jpg?h=c6980913&itok=2lgbSBjY)
Kate Evans, director for the Computational Sciences and Engineering Division at ORNL, has been awarded the 2024 Society for Industrial and Applied Mathematicians Activity Group on Mathematics of Planet Earth Prize.
![A multidirectorate group from ORNL attended AGU23 and came away inspired for the year ahead in geospatial, earth and climate science](/sites/default/files/styles/list_page_thumbnail/public/2024-02/MicrosoftTeams-image%20%2815%29%20%281%29.png?h=a5eb5da0&itok=gY269KaC)
ORNL scientists and researchers attended the annual American Geophysical Union meeting and came away inspired for the year ahead in geospatial, earth and climate science.
![: ORNL climate modeling expertise contributed to an AI-backed model that assesses global emissions of ammonia from croplands now and in a warmer future, while identifying mitigation strategies. This map highlights croplands around the world. Credit: U.S. Geological Survey](/sites/default/files/styles/list_page_thumbnail/public/2024-02/global_croplands_usgs_globe-4g_1.png?h=4016a495&itok=rb8eHyvK)
ORNL climate modeling expertise contributed to a project that assessed global emissions of ammonia from croplands now and in a warmer future, while also identifying solutions tuned to local growing conditions.