Two PNNL team members, Courtney Corley, a data scientist, and Kyle Bingman, an advisor on assured artificial intelligence (AI), were featured on a recent episode of the U.S. Department of Energy Direct Currents podcast.
Ten staff members from PNNL were invited to attend and lead the various breakout sessions at the Department of Energy Office of Science 5G Enabled Energy Innovation Workshop (5GEEIW), which was held in early March.
Two PNNL researchers are helping define the future of transparency and accountability for public and private use of autonomous and intelligent systems.
PNNL researchers and professional staff led discussions ranging from biothreats and climate change to science careers at the 2020 annual meeting of the American Association for the Advancement of Science, held this year in Seattle.
First-of-its-kind network analysis on a supercomputer can speed real-time applications for cybersecurity, transportation, and infectious disease tracking
Bill Cannon, senior scientist and biophysicist in the Computational Mathematics Group, was a co-author of a recent article published in Nature Partner Journals-Digital Medicine.
PNNL and the 13 other national laboratories of the Grid Modernization Laboratory Consortium (GMLC) will be sharing their R&D work and technologies for grid modernization at DistribuTECH International in San Antonio Jan. 28-30.
Nicole Nichols, a senior researcher at PNNL, spoke during the AI: Policy Matters Summit in Seattle, Washington on December 12. The summit, hosted by TechAlliance, brought together more than 200 leaders from across Washington State.
Sonja Glavaski and Kevin Schneider, both electrical engineers at PNNL, have been named as IEEE fellows. IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
A group of female mathematicians and computer scientists, which includes PNNL’s Emilie Purvine, has published its third paper on joint research to understand and accurately represent object relationships through metric graphs.
PNNL will lead three new grid modernization projects funded by the Department of Energy. The projects focus on scalability and usability, networked microgrids, and machine learning for a more resilient, flexible and secure power grid.
At a conference featuring the most advanced computing hardware and software, ML in its various guises was on full display and highlighted by Nathan Baker’s featured invited presentation.
Through her role in the Department of Energy’s Advanced Scientific Computing Research-supported ExaLearn project, Jenna Pope is developing deep learning approaches for finding optimal water cluster structures for a variety of applications.
Scientists at PNNL are bringing artificial intelligence into the quest to see whether computers can help humans sift through a sea of experimental data.
In today’s digital age, the rabbit hole of connected information can be not only a time sink, but downright overwhelming. Even for high-performance computers.
Twenty-four analysts from U.S. intelligence organizations met in August for a machine learning activity with PNNL researchers Nicole Nichols, Jeremiah Rounds, Lawrence Phillips, and Brian Kritzstein.
Trouble on the electric grid might start with something relatively small: a downed power line, or a lightning strike at a substation. What happens next?