The AI-Driven One Health Security activities at PNNL are focused on disrupting health threats and their impacts.

PNNL partners with the Department of Homeland Security, industry, and the first-responder community to improve the effectiveness and safety during biodetection responses.

The Center for AI @PNNL is driving a research agenda that explores the foundations and emerging frontiers of AI, combining capability development and application to mission areas in science, security and energy resilience.

PNNL and ORNL are working together on Digital Twins to modernize the U.S. hydropower plant fleet, which will reduce operating costs, improve reliability, reduce downtime, enhance grid resiliency, and reduce environmental impacts.

Usable and explainable models for global-scale, cross-lingual proliferation expertise identification and forecasting.

PNNL is heavily engaged in the development and use of mass spectrometry technology across its science, energy, and security missions, from fundamental research through mature operational capabilities.

Pacific Northwest National Laboratory supports innovations in data analytics, instrumentation, and experimental techniques for the Northwest (NW) Biopreparedness Research Virtual Environment (BRaVE) Initiative.

Physics-informed machine learning (PIML) is a modeling approach that harnesses the power of machine learning and big data to improve the understanding of coupled, dynamic systems.

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.

PNNL computer scientists are presenting the latest research in HPC at SC20.

Using a reverse genome strategy to target the molecular basis of function.

PNNL combines AI and cloud computing with damage assessment tools to predict the path of wildfires and quickly evaluate the impact of natural disasters, giving first responders an upper hand.

PNNL has developed a tool suite of interactive analytics that can be rapidly integrated into analyst workflows to empirically analyze and gain qualitative understanding of AI model performance jointly across dimensions.

The Water Cycle and Climate Extremes Modeling (WACCEM) Scientific Focus Area advances predictive understanding of water cycle variability and change through foundational research using models, observations, and novel numerical experiments.