Slaven Peles, PNNL computational scientist and leader of a national high-performance computing project for power grid analysis, spoke about the project with the host of the Let’s Talk Exascale podcast.
A team of PNNL researchers are looking at how to evaluate robustness and accountability, fairness, and transparency of artificial intelligence models used to detect and quantify deceptive content online.
A Q&A with Lauren Charles, veterinarian and PNNL data scientist, on zoonotic diseases and the role biosurveillance plays in mitigating the growing threat to global health.
More than 30 PNNL interns contributed to the Airport Risk Assessment Model, a web-based tool that helps airport security stakeholders prioritize resource allocations.
PNNL’s data-infused approach to electron microscopes’ use in scientific experimentation will help researchers and industry interpret large data streams and drive down costs.
PNNL cybersecurity engineer Penny McKenzie was selected from hundreds of national laboratory mentors to join Secretary of Energy Jennifer Granholm on multi-laboratory DOE internship panel for summer interns.
PNNL combines AI and cloud computing with damage assessment tool to predict path of wildfires and quickly evaluate the impact of natural disasters, giving first responders an upper hand.
PNNL recently worked with Purdue University to host a Cybersecurity Summit for PNNL researchers to find out more about the research at Purdue’s Center for Education and Research in Information Assurance and Security.
Machine learning techniques are accelerating the development of stronger alloys for power plants, which will yield efficiency, cost, and decarbonization benefits.
A research project that brings together mathematicians and atmospheric scientists has developed into a deep collaboration for improving atmospheric models.
Svitlana Volkova, chief scientist for decision intelligence and analytics at PNNL, was invited as a panelist at the SIAM International Conference on Data Mining