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The Department of Energy’s Oak Ridge National Laboratory announced the establishment of its Center for AI Security Research, or CAISER, to address threats already present as governments and industries around the world adopt artificial intelligence and take advantage of the benefits it promises in data processing, operational efficiencies and decision-making. Credit: Rachel Green/ORNL, U.S. Dept. of Energy

The Department of Energy’s Oak Ridge National Laboratory announced the establishment of the Center for AI Security Research, or CAISER, to address threats already present as governments and industries around the world adopt artificial intelligence and take advantage of the benefits it promises in data processing, operational efficiencies and decision-making.

Director of ORNL’s AI Initiative Prasanna Balaprakash addresses attendees at the Generative AI for ORNL Science Workshop. Credit: ORNL, U.S. Dept. of Energy

The Department of Energy’s Oak Ridge National Laboratory hosted its Smoky Mountains Computational Science and Engineering Conference for the first time in person since the COVID pandemic broke in 2020. The conference, which celebrated its 20th consecutive year, took place at the Crowne Plaza Hotel in downtown Knoxville, Tenn., in late August.

Tristen Mullins. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Tristen Mullins enjoys the hidden side of computers. As a signals processing engineer for ORNL, she tries to uncover information hidden in components used on the nation’s power grid — information that may be susceptible to cyberattacks.

UnifyFS team wins IPDPS award for open-source software

A research team from the Department of Energy’s Oak Ridge and Lawrence Livermore national laboratories won the first Best Open-Source Contribution Award for its paper at the 37th IEEE International Parallel and Distributed Processing Symposium.

ORNL seismic researcher Chengping Chai placed seismic sensors on the ground at various distances from an ORNL nuclear reactor to learn whether they could detect its operating state. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Like most scientists, Chengping Chai is not content with the surface of things: He wants to probe beyond to learn what’s really going on. But in his case, he is literally building a map of the world beneath, using seismic and acoustic data that reveal when and where the earth moves.

A new license to U2opia pairs two technologies developed in ORNL’s Cyber Resilience and Intelligence Division: Situ and Heartbeat. Credit: ORNL, U.S. Dept. of Energy

U2opia Technology, a consortium of technology and administrative executives with extensive experience in both industry and defense, has exclusively licensed two technologies from ORNL that offer a new method for advanced cybersecurity monitoring in real time.

Watermarks, considered the most efficient mechanisms for tracking how complete streaming data processing is, allow new tasks to be processed immediately after prior tasks are completed. Image Credit: Nathan Armistead, ORNL

A team of collaborators from ORNL, Google Inc., Snowflake Inc. and Ververica GmbH has tested a computing concept that could help speed up real-time processing of data that stream on mobile and other electronic devices.

An open-source code developed by an ORNL-led team could provide new insights into the everyday operation of the nation’s power grid. Credit: Pixabay

Oak Ridge National Laboratory, University of Tennessee and University of Central Florida researchers released a new high-performance computing code designed to more efficiently examine power systems and identify electrical grid disruptions, such as

ORNL’s particle entanglement machine is a precursor to the device that researchers at the University of Oklahoma are building, which will produce entangled quantum particles for quantum sensing to detect underground pipeline leaks. Credit: ORNL, U.S. Dept. of Energy

To minimize potential damage from underground oil and gas leaks, Oak Ridge National Laboratory is co-developing a quantum sensing system to detect pipeline leaks more quickly.

An algorithm developed and field-tested by ORNL researchers uses machine learning to maintain homeowners’ preferred temperatures year-round while minimizing energy costs. Credit: ORNL, U.S. Dept. of Energy

Oak Ridge National Laboratory researchers designed and field-tested an algorithm that could help homeowners maintain comfortable temperatures year-round while minimizing utility costs.