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Media Contacts
A crowd of investors and supporters turned out for last week’s Innovation Crossroads Showcase at the Knoxville Chamber as part of Innov865 Week. Sponsored by ORNL and the Tennessee Advanced Energy Business Council, the event celebrated deep-tech entrepreneurs and the Oak Ridge Corridor as a growing energy innovation hub for the nation.
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.
How an Alvin M. Weinberg Fellow is increasing security for critical infrastructure components
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.
A study led by researchers at ORNL could help make materials design as customizable as point-and-click.
A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.
As technology continues to evolve, cybersecurity threats do as well. To better safeguard digital information, a team of researchers at the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory (ORNL) has developed Akatosh, a security analysis tool that works in conjunctio...
As leader of the RF, Communications, and Cyber-Physical Security Group at Oak Ridge National Laboratory, Kerekes heads an accelerated lab-directed research program to build virtual models of critical infrastructure systems like the power grid that can be used to develop ways to detect and repel cyber-intrusion and to make the network resilient when disruption occurs.