Though Nell Barber wasn’t sure what her future held after graduating with a bachelor’s degree in psychology, she now uses her interest in human behavior to design systems that leverage machine learning algorithms to identify faces in a crowd.
University of Pennsylvania researchers called on computational systems biology expertise at Oak Ridge National Laboratory to analyze large datasets of single-cell RNA sequencing from skin samples afflicted with atopic dermatitis.
An international problem like climate change needs solutions that cross boundaries, both on maps and among disciplines. Oak Ridge National Laboratory computational scientist Deeksha Rastogi embodies that approach.
Deborah Frincke, one of the nation’s preeminent computer scientists and cybersecurity experts, serves as associate laboratory director of ORNL’s National Security Science Directorate. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy
A new tool from Oak Ridge National Laboratory can help planners, emergency responders and scientists visualize how flood waters will spread for any scenario and terrain.
Scientists have tapped the immense power of the Summit supercomputer at Oak Ridge National Laboratory to comb through millions of medical journal articles to identify potential vaccines, drugs and effective measures that could suppress or stop the
A novel approach developed by scientists at ORNL can scan massive datasets of large-scale satellite images to more accurately map infrastructure – such as buildings and roads – in hours versus days.
The prospect of simulating a fusion plasma is a step closer to reality thanks to a new computational tool developed by scientists in fusion physics, computer science and mathematics at ORNL.
To better determine the potential energy cost savings among connected homes, researchers at Oak Ridge National Laboratory developed a computer simulation to more accurately compare energy use on similar weather days.
In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly.