Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.

PNNL’s Andrew Engel, Tony Chiang, and Sutanay Choudhury at the 2024 ICLR in Vienna.

PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.

A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.

In a new approach, quantum flow algorithms are used to simulate many-body systems on quantum computers and effectively describe chemical processes.

PNNL scientists have been studying how rivers and streams breathe. Their research focuses on respiration, organic matter, and natural disturbances that affect rivers and streams.

PNNL chief scientist and joint appointee Auroop Ganguly was recently appointed a Distinguished Member of the Association for Computing Machinery, a high honor from the world's largest computer science society.

Samantha Erwin, a PNNL data scientist, was recently appointed to the Society for Industrial and Applied Mathematics Membership Committee.

PNNL’s Climate Network with MSIs and HBCUs aims to bring opportunities to diverse populations.

Brett Jefferson, data scientist, was recently recognized for his determination and success in his research space with an Early Career Award from Indiana University Bloomington in the Psychological and Brain Sciences Department.

The convergence of artificial intelligence, cloud, and high-performance computing to accelerate scientific discovery is the focus of a multi-year collaboration between Microsoft and PNNL.

PNNL researchers comment on the “Adversarial Machine Learning” white paper from the National Institute of Standards and Technology.

PNNL has created the Center for AI @PNNL to coordinate the pioneering research of hundreds of scientists working on a range of projects in artificial intelligence.

Making it on CrystEngComm’s HOT list, the article, “Designing scintillating coordination polymers using a dual-ligand synthetic approach,” highlights research on existing materials that are non-traditional scintillators.

PNNL is one of the founding partners of the Trillion Parameter Consortium, which was formed to create reliable generative AI models.

Input from humans helps when deciding whether to trust the recommendations or decisions of a machine-learning system.

PNNL’s DataLife tool analyzes and identifies bottlenecks in scientific workflows.

The use of disciplines in pure mathematics can increase the reliability and explainability of machine learning models that “transcend human intuition,” according to PNNL scientists.

Lauren Charles has been named a scientist and engineer level 5 in the national security domain.

Lynn Wood has been named a scientist and engineer level 5 in the national security domain.