Robert Hettich: Decoding biological complexity with next-gen mass spectrometry
Massimiliano (Max) Lupo Pasini, an R&D data scientist from ORNL, was awarded the National Energy Research Scientific Computing Center’s High Performance Computing Achievement Award for High Impact Scientific Achievement for his work in “Groundbreaking contributions to scientific machine learning, particularly through the development of HydraGNN.”
Scientists at the Department of Energy’s Oak Ridge National Laboratory recently demonstrated an autonomous robotic field monitoring, sampling and data-gathering system that could accelerate understanding of interactions among plants, soil and the environment.
To bridge the gap between experimental facilities and supercomputers, experts from SLAC National Accelerator Laboratory are teaming up with other DOE national laboratories to build a new data streaming pipeline. The pipeline will allow researchers to send their data to the nation’s leading computing centers for analysis in real time even as their experiments are taking place.
A new Global Biomass Resource Assessment developed by ORNL scientists gathered data from 55 countries resulting in a first-of-its kind compilation of current and future sustainable biomass supply estimates around the world.
A study led by the Department of Energy’s Oak Ridge National Laboratory details how artificial intelligence researchers created an AI model to help identify new alloys used as shielding for housing fusion applications components in a nuclear reactor. The findings mark a major step towards improving nuclear fusion facilities.
Debjani Singh, a senior scientist at ORNL, leads the HydroSource project, which enhances hydropower research by making water data more accessible and useful. With a background in water resources, data science, and earth science, Singh applies innovative tools like AI to advance research. Her career, shaped by her early exposure to science in India, focuses on bridging research with practical applications.
A study found that beaches with manmade fortifications recover more slowly from hurricanes than natural beaches, losing more sand and vegetation. The researchers used satellite images and light detection and ranging data, or LIDAR, to measure elevation changes and vegetation coverage. Changes in elevation showed how much sand was depleted during the storm and how much sand returned throughout the following year.
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.
Researchers at the Department of Energy’s Oak Ridge National Laboratory met recently at an AI Summit to better understand threats surrounding artificial intelligence. The event was part of ORNL’s mission to shape the future of safe and secure AI systems charged with our nation’s most precious data.
Held in Cocoa Beach, Florida from March 11 to 14, researchers across the computing and data spectra participated in sessions developed by staff members from the Department of Energy’s Oak Ridge National Laboratory, or ORNL, Sandia National Laboratories and the Swiss National Supercomputing Centre.