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Joon-Seok Kim Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

Researchers at ORNL are using a machine-learning model to answer ‘what if’ questions stemming from major events that impact large numbers of people. By simulating an event, such as extreme weather, researchers can see how people might respond to adverse situations, and those outcomes can be used to improve emergency planning.

Jiafu Mao, left, and Yaoping Wang discuss their analysis of urban and rural vegetation resilience across the United States in the EVEREST visualization lab at ORNL. Credit: Carlos Jones, ORNL/U.S. Dept. of Energy

Scientists at ORNL completed a study of how well vegetation survived extreme heat events in both urban and rural communities across the country in recent years. The analysis informs pathways for climate mitigation, including ways to reduce the effect of urban heat islands.

Quietly making noise: Measuring differential privacy could balance meaningful analytics and identity protection

To balance personal safety and research innovation, researchers at ORNL are employing a mathematical technique known as differential privacy to provide data privacy guarantees.

The AI for Energy Report provides a framework for using AI to accelerate decarbonization of the U.S. economy. Credit: Argonne National Laboratory

Groundbreaking report provides ambitious framework for accelerating clean energy deployment while minimizing risks and costs in the face of climate change.

The transportation and industrial sectors together account for more than 50% of the country’s carbon footprint. Defossilization could help reduce new emissions from these and other difficult-to-electrify segments of the U.S. economy.

Scientists at Oak Ridge National Laboratory and six other Department of Energy national laboratories have developed a United States-based perspective for achieving net-zero carbon emissions. 

Chengyun Hua

The Quantum Voices series is designed to share the stories of the quantum researchers and technical experts behind the Quantum Science Center’s past, present and future accomplishments. Chengyun Hua is highlighted for this edition, talking about her role in the Quantum Science Center. 

Researchers relied on support from ORNL’s Quantum Computing User Program to simulate a key quantum state at one of the largest scales reported. The findings could mark a step toward improving quantum simulations.  Credit: Getty Images

Researchers simulated a key quantum state at one of the largest scales reported, with support from the Quantum Computing User Program, or QCUP, at ORNL. 

SOS26 attendees standing in front of the Kennedy Space Center on Merrit Island, Florida the night of their dinner reception provided by the conference sponsors. The keynote speaker was Rupak Biswas from NASA. Credit: Judy Potok/ORNL, U.S. Dept. of Energy

Held in Cocoa Beach, Florida from March 11 to 14, researchers across the computing and data spectra participated in sessions developed by staff members from the Department of Energy’s Oak Ridge National Laboratory, or ORNL, Sandia National Laboratories and the Swiss National Supercomputing Centre. 

ORNL quantum researchers, from left, Brian Williams, Phil Evans, and Nick Peters work on their quantum key distribution system.

ORNL scientists have spent the past 20 years studying quantum photonic entanglement. Their partnership with colleagues at Los Alamos National Laboratory and private industry partner Qubitekk led to development of the nation’s first industry-led commercial quantum network. This type of network could ultimately help secure the nation’s power grid and other infrastructure from cyberattacks.

ORNL’s Suhas Sreehari explains the algebraic and topological foundations of representation systems, used in generative AI technology such as large language models. Credit: Lena Shoemaker/ORNL, U.S. Dept. of Energy

In the age of easy access to generative AI software, user can take steps to stay safe. Suhas Sreehari, an applied mathematician, identifies misconceptions of generative AI that could lead to unintentionally bad outcomes for a user.