Jacobson elected Fellow of the American Institute for Medical and Biological Engineering
John Lagergren, a staff scientist in 91做厙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 ORNLs Advanced Plant Phenotyping Laboratory.
Researchers tackling national security challenges at ORNL are upholding an 80-year legacy of leadership in all things nuclear. Today, theyre developing the next generation of technologies that will help reduce global nuclear risk and enable safe, secure, peaceful use of nuclear materials, worldwide.
A team led by researchers at ORNL explored training strategies for one of the largest artificial intelligence models to date with help from the worlds fastest supercomputer. The findings could help guide training for a new generation of AI models for scientific research.
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.
Groundwater withdrawals are expected to peak in about one-third of the worlds basins by 2050, potentially triggering significant trade and agriculture shifts, a new analysis finds.
ORNL researchers modeled how hurricane cloud cover would affect solar energy generation as a storm followed 10 possible trajectories over the Caribbean and Southern U.S.
To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance.
New computational framework speeds discovery of fungal metabolites, key to plant health and used in drug therapies and for other uses.
In summer 2023, ORNL's Prasanna Balaprakash was invited to speak at a roundtable discussion focused on the importance of academic artificial intelligence research and development hosted by the White House Office of Science and Technology Policy and the U.S. National Science Foundation.
Lee's paper at the August conference in Bellevue, Washington, combined weather and power outage data for three states Texas, Michigan and Hawaii and used a machine learning model to predict how extreme weather such as thunderstorms, floods and tornadoes would affect local power grids and to estimate the risk for outages. The paper relied on data from the National Weather Service and the U.S. Department of Energys Environment for Analysis of Geo-Located Energy Information, or EAGLE-I, database.