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.
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 data scientists Maria Glenski and Svitlana Volkova have contributed a chapter to a book titled Disinformation, Misinformation, and Fake News in Social Media: Emerging Research Challenges and Opportunities.
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.
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.
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.
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.