PNNL Staff Featured on U.S. Department of Energy Podcast
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.
Synthetic Data and Graph Generation for Modeling Adversarial Activity – Final Project Report
PNNL Data Scientists Co-Author Book Chapter about User Engagement with Deceptive News on Social Media
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.
PNNL @ NeurIPS 2020
PNNL data scientists and engineers will be presenting at NeurIPS, the Thirty Fourth Conference on Neural Information Processing Systems, and the co-located Women in Machine Learning workshop, WiML.
Keeping a Data Analytics Conference Current
Long-term PNNL support has helped enable the continued success of the Chesapeake Large-Scale Analytics Conference.
National Laboratory Pulls Together to Address the COVID-19 Pandemic
PNNL has increased the nation’s capacity to test for COVID-19, demonstrating that reagents and equipment from additional manufacturers meet the standards needed to yield accurate test results.
Decoding Protein Interactions with Domain-Aware Machine Learning
Parallel graph neural networks identify protein-ligand 3-D structural interactions to aid in protein function prediction and drug discovery.
A New Look at One of the Most Abundant Particles in the Universe
Researchers at PNNL are applying deep learning techniques to learn more about neutrinos, part of a worldwide network of researchers trying to understand one of the universe’s most elusive particles.