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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.](/sites/default/files/styles/list_page_thumbnail/public/2019-03/TourassiWH%5B1%5D.png?h=26b5064d&itok=HUC2iYmE)
OAK RIDGE, Tenn., March 4, 2019—A team of researchers from the Department of Energy’s Oak Ridge National Laboratory Health Data Sciences Institute have harnessed the power of artificial intelligence to better match cancer patients with clinical trials.
![As part of a preliminary study, ORNL scientists used critical location data collected from Twitter to map the location of certain power outages across the United States.](/sites/default/files/styles/list_page_thumbnail/public/2019-02/PowerOutageTweets_map_0.png?h=6448fdc1&itok=AUit-O2Y)
Gleaning valuable data from social platforms such as Twitter—particularly to map out critical location information during emergencies— has become more effective and efficient thanks to Oak Ridge National Laboratory.
OAK RIDGE, Tenn., Feb. 12, 2019—A team of researchers from the Department of Energy’s Oak Ridge and Los Alamos National Laboratories has partnered with EPB, a Chattanooga utility and telecommunications company, to demonstrate the effectiveness of metro-scale quantum key distribution (QKD).
![Researchers used machine learning methods on the ORNL Compute and Data Environment for Science, or CADES, to map vegetation communities in the Kougarok Watershed on the Seward Peninsula of Alaska. The colors denote different types of vegetation, such as w Researchers used machine learning methods on the ORNL Compute and Data Environment for Science, or CADES, to map vegetation communities in the Kougarok Watershed on the Seward Peninsula of Alaska. The colors denote different types of vegetation, such as w](/sites/default/files/styles/list_page_thumbnail/public/rs2019_highlight_plot_3d.png?itok=5bROV_ys)
A team of scientists led by Oak Ridge National Laboratory used machine learning methods to generate a high-resolution map of vegetation growing in the remote reaches of the Alaskan tundra.
![X1800-REED-Maritime Risk Symposium 2018 logo-AM V5-01.jpg X1800-REED-Maritime Risk Symposium 2018 logo-AM V5-01.jpg](/sites/default/files/styles/list_page_thumbnail/public/X1800-REED-Maritime%20Risk%20Symposium%202018%20logo-AM%20V5-01.jpg?itok=_AN4HV63)
Thought leaders from across the maritime community came together at Oak Ridge National Laboratory to explore the emerging new energy landscape for the maritime transportation system during the Ninth Annual Maritime Risk Symposium.
![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’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.](/sites/default/files/styles/list_page_thumbnail/public/news/images/RAvENNA%20release%20pic.png?itok=2bDpK5Mo)
A team of researchers from the Department of Energy’s Oak Ridge National Laboratory 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
![Arjun Shankar Arjun Shankar](/sites/default/files/styles/list_page_thumbnail/public/shankar.png?itok=qqOR_eUI)
The field of “Big Data” has exploded in the blink of an eye, growing exponentially into almost every branch of science in just a few decades. Sectors such as energy, manufacturing, healthcare and many others depend on scalable data processing and analysis for continued in...