PNNL commemorates 9/11 and reflects on the 20 years of science and technology produced since to protect against threats and make America safer.

PNNL computer scientist Ang Li serves as one of the principal investigators for a project selected to receive DOE funding.

Hypergraphs can more faithfully identify important genes in complex immune responses.

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.

The web-based tool, called PLATIPUS, is publicly available and interacts with publications available in the CovidScholar database.

Seeking a deeper understanding of when, where, and why large wildfires occur using explainable artificial intelligence.

When training for chemical and biological threats, in-person exercises and drills can be very limited due to the hazardous nature of the agent.

PNNL’s new Hydrogen Energy Storage Evaluation Tool allows users to examine multiple energy delivery pathways and grid applications to maximize benefits.

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.

At PNNL, women in engineering tackle some of the world’s most complex problems in environmental science, sustainable energy, and national security.

Svitlana Volkova uses natural language processing and AI to reveal how misinformation and disinformation spread through social media.

Researchers to design their own computer accelerators for faster analysis of large datasets.

OpenCGRA expedites the design-prototype cycle of computer architecture development.

Scientists have created a cybersecurity technology that stops hackers from doing damage by feeding them illusory tidbits of success.

The multi-year project funded by DOE’s High Energy Physics program will focus on using machine learning to prevent power outages.

Risk analysis on the plutonium-fueled power system that supplies electricity to the Mars rover answered the “what if” nuclear safety questions for NASA.

Marcel Baer is a computational scientist working in PNNL’s Physical Sciences Division with a prominent effort in materials science and physical bioscience.

New computational modeling research provides insights into biological processes and metabolic regulation.

In the face of the COVID-19 pandemic, PNNL pivoted to online learning approaches to conduct critical national security training across the globe.

PNNL computer scientists joined international leaders in machine learning to present research to detect and address potential cybersecurity threats and devise epidemic interventions.