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.
Sriram Krishnamoorthy, a computer scientist at PNNL, collaborated with a University of Utah team on a student computing research project that won Best Student Paper at SC20.
As a member of the NAM board of directors, Brett Jefferson, PNNL data scientist, will help lead the professional association’s mission to advance mathematical excellence of underrepresented minorities.
PNNL-developed Water Balance Tool estimates consumption for major water end-uses. Understanding the breakout of water use identifies water efficiency opportunities and allows facility managers to spot potential system losses.
New Distinguished Graduate Research Program will provide opportunities for North Carolina State University doctoral students to tackle real-world data science challenges alongside PNNL scientists.
PNNL created an assessment method and maturity model that helps manufacturers building products for the power grid implement consistent cybersecurity best practices throughout their development lifecycle.
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.
Ann Lesperance, national security advisor, joins the National Academies of Sciences, Engineering, and Medicine Committee on Applied Research Topics for Hazard Mitigation and Resilience.
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.
As COVID-19 was limiting in-person contact, halting travel, and creating additional barriers, researchers at PNNL were working to find solutions on how they could still get work done while establishing new safety protocols.
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.
The project received an Innovative and Novel Computational Impact on Theory and Experiment (INCITE) award, a highly competitive U.S. Department of Energy Office of Science program.