PNNL is leading the nation with research addressing urgent needs for reimagining U.S. critical infrastructure against the realities of software-speed attacks and hazards.
PNNL partnered with the Treasury and AWS to develop Cache, a cloud-based tool that allows the Treasury’s disparate data to be easily searched, translated, extracted, linked, and analyzed.
Cyber, physical, and blended cyber-physical threats are real, ubiquitous, and expensive to deal with. Private companies, government institutions, and critical infrastructures struggle to implement viable solutions as technology evolves.
The CVAir pilot project transports computed tomography passenger baggage data from originating international airports for targeted flights destined for the U.S.
Cyber networks are constantly under attack by bugs, bots, and nefarious actors. While system owners acutely understand the need to secure their networks, they’re not always sure of the best actions to take.
In January 2024, CESER—in partnership with GDO, NASEO, and PNNL—created a set of state energy security cohorts to support the coordination and technical development of state energy security planning, assessment, and mitigation.
A multi-institution research team led by PNNL is addressing curb usage management challenges in large urban areas by developing a city-scale dynamic curb use simulation tool and an open-source curb management platform.
GeoBOSS is a software library that combines the data-handling capabilities of Spark and the user-friendliness of Python to simplify geospatial analytics and the transition between small-scale research and large-scale operational projects.
Our nation’s critical infrastructure supports the security and wellbeing of our society. Maintaining the resilience of important markets and services is vital to upholding our way of life.
The RD2C laboratory-directed research initiative seeks to develop resilient, adaptive, and intelligent sensing and control algorithms through the observational understanding and characterization of CPSs under adverse conditions.
Powered by few-shot learning, the Sharkzor AI-driven, scalable web application makes it possible to quickly characterize and sort electron microscopy images used to analyze radioactive materials.
