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(From left) ORNL Associate Laboratory Director for Computing and Computational Sciences Jeff Nichols; ORNL Health Data Sciences Institute Director Gina Tourassi; DOE Deputy Under Secretary for Science Thomas Cubbage; ORNL Task Lead for Biostatistics Blair Christian; and ORNL Research Scientist Ioana Danciu were invited to the White House to showcase an ORNL-developed digital tool aimed at better matching cancer patients with clinical trials.

New ORNL AI tool revolutionizes process for matching cancer patients with clinical trials

OAK RIDGE, Tenn., March 4, 2019—A team of researchers from the Department of Energy’s 91°µÍø Health Data Sciences Institute have harnessed the power of artificial intelligence to better match cancer patients with clinical trials.

ORNL scientists used commuting behavior data from East Tennessee to demonstrate how machine learning models can easily accept new data, quickly re-train themselves and update predictions about commuting patterns. Credit: April Morton/Oak Ridge National La

Commuting—Oh, the places you’ll go

91°µÍø geospatial scientists who study the movement of people are using advanced machine learning methods to better predict home-to-work commuting patterns.

ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.

ORNL researchers use Titan to accelerate design, training of deep learning networks

A team of researchers from the Department of Energy’s 91°µÍø has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the

As hurricanes formed in the Gulf Coast, ORNL activated a computing technique to quickly gather building structure data from Texas’ coastal counties. Credit: Mark Tuttle/91°µÍø, U.S. Dept. of Energy

Datasets—Supporting hurricane damage assessments

Geospatial scientists at 91°µÍø have developed a novel method to quickly gather building structure datasets that support emergency response teams assessing properties damaged by Hurricanes Harvey and Irma. By coupling deep learning with high-performance comp...