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.
To optimize biomaterials for reliable, cost-effective paper production, building construction, and biofuel development, researchers often study the structure of plant cells using techniques such as freezing plant samples or placing them in a vacuum.
Scientists develop environmental justice lens to identify neighborhoods vulnerable to climate change
A new capability to identify urban neighborhoods, down to the block and building level, that are most vulnerable to climate change could help ensure that mitigation and resilience programs reach the people who need them the most.
How an Alvin M. Weinberg Fellow is increasing security for critical infrastructure components
Jennifer Morrell-Falvey’s interest in visualizing the science behind natural processes was what drew her to ORNL in what she expected to be a short stint some 18 years ago.
It’s a simple premise: To truly improve the health, safety, and security of human beings, you must first understand where those individuals are.
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.
Tackling the climate crisis and achieving an equitable clean energy future are among the biggest challenges of our time.
ORNL, TVA and TNECD were recognized by the Federal Laboratory Consortium for their impactful partnership that resulted in a record $2.3 billion investment by Ultium Cells, a General Motors and LG Energy Solution joint venture, to build a battery cell manufacturing plant in Spring Hill, Tennessee.
Three ORNL scientists have been elected fellows of the American Association for the Advancement of Science, or AAAS, the world’s largest general scientific society and publisher of the Science family of journals.