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ORNL researchers developed a long-sequenced AI transformer capable of processing millions of pathology reports to provide experts researching cancer diagnoses and management with more accurate information on cancer reporting.

In partnership with the National Cancer Institute, researchers from ORNL and Louisiana State University developed a long-sequenced AI transformer capable of processing millions of pathology reports to provide experts researching cancer diagnoses and management with exponentially more accurate information on cancer reporting.

ORNL’s Kate Evans has been awarded the 2024 Society for Industrial and Applied Mathematicians Activity Group on Mathematics of Planet Earth Prize.

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. 

Anuj Kapadia

Anuj J. Kapadia, who heads the Advanced Computing Methods for Health Sciences Section at ORNL, has been elected as president of the Southeastern Chapter of the American Association of Physicists in Medicine. 

New system combines human, artificial intelligence to improve experimentation

To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance. 

: 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

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.

Using a better modeling framework, with data collected from Mississippi Delta marshes, scientists are able to improve the predictions of methane and other greenhouse gas emissions. Credit: Matthew Berens/ORNL, U.S Dept. of Energy

Scientists at the Department of Energy’s Oak Ridge National Laboratory are using a new modeling framework in conjunction with data collected from marshes in the Mississippi Delta to improve predictions of climate-warming methane and nitrous oxide

ORNL Associate Laboratory Director for Computing and Computational Sciences. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Gina Tourassi, associate laboratory director for computing and computational sciences at the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory, has been named a fellow of the Institute of Electrical and Electronics Engineers, the world’s largest organization for technical professionals.

The illustration depicts ocean surface currents simulated by MPAS-Ocean. Credit: Los Alamos National Laboratory, E3SM, U.S. Dept. of Energy

A team from DOE’s Oak Ridge, Los Alamos and Sandia National Laboratories has developed a new solver algorithm that reduces the total run time of the Model for Prediction Across Scales-Ocean, or MPAS-Ocean, E3SM’s ocean circulation model, by 45%. 

Gina Tourassi. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy 

Effective Dec. 4, Gina Tourassi will assume responsibilities as associate laboratory director for the Computing and Computational Sciences Directorate at the Department of Energy’s Oak Ridge National Laboratory.

Frontier, the fastest supercomputer in the world, provides expansive and energy-efficient power, which gives scientists the capability to train large AI models in a responsible way.

ORNL is home to the world's fastest exascale supercomputer, Frontier, which was built in part to facilitate energy-efficient and scalable AI-based algorithms and simulations.