One year ago, Verizon announced a partnership that made PNNL the U.S. Department of Energy’s first national laboratory with Verizon 5G ultra-wideband wireless technology.
Sentry-SECURE is a new communication and response platform developed by PNNL, VPI, and Microsoft Azure that rapidly and securely transfers radiological alarm data through the cloud.
Michael Henry, a senior data scientist at PNNL, has accepted a joint appointment at the Texas A&M University RELLIS Center for Applied Research and Experiential Learning.
(ISC)², the world’s largest nonprofit association of cybersecurity professionals, elected PNNL cybersecurity expert Lori Ross O’Neil as vice chairperson of the board of directors.
PNNL data scientists Henry Kvinge and Ted Fujimoto presented their research on few-shot learning and reinforcement learning, respectively, at workshops during the 2021 AAAI Conference on Artificial Intelligence.
New mathematical tools developed at PNNL hold promise to transform the way we operate and defend complex cyber-physical systems, such as the power grid.
The partnership to apply artificial intelligence to improve complex systems is part of a U.S. Department of Energy Office of Science $4.2 million, three-year grant.
PNNL scientists joined international leaders in artificial intelligence research to discuss the latest advances, opportunities, and challenges for neural information processing—the foundation for AI.
Red teaming for CPS, the process of challenging systems, involves a group of cybersecurity experts to emulate end-to-end cyberattacks following a set of realistic tactics, techniques, and procedures.
PNNL computational biologists, structural biologists, and analytical chemists are using their expertise to safely accelerate the design step of the COVID-19 drug discovery process.
A special issue of the Marine Technology Society Journal, titled “Utilizing Offshore Resources for Renewable Energy Development,” focuses on research and development efforts including those at Pacific Northwest National Laboratory (PNNL).
Using public data from the entire 1,500-square-mile Los Angeles metropolitan area, PNNL researchers reduced the time needed to create a traffic congestion model by an order of magnitude, from hours to minutes.
PNNL researchers have shown an improved binarized neural network can deliver a low-cost and low-energy computation to help the performance of smart devices and the power grid.