Human-machine teaming may sound like something from the distant future. In “Human-Machine Teaming: A Vision of Future Law Enforcement” in Domestic Preparedness, Corey Fallon, Kris Cook, and Grant Tietje of PNNL examine this topic.
A PNNL study has shown the nation’s wastewater resource recovery facilities could generate revenue by converting sludge into biofuel—while significantly reducing disposal costs—using an in-house-developed technology.
PNNL is highlighting scientific and technical experts in the national security domain who were recently promoted to scientist and engineer Level 5, one of PNNL’s most senior research roles.
PNNL has received 119 R&D 100 Awards since 1969, when the laboratory began submitting entries in the contest that recognizes top 100 inventions each year.
Scott Chambers creates layered structures of thin metal oxide films and studies their properties, creating materials not found in nature. He will soon move his instrumentation and research to the new Energy Sciences Center.
PNNL data scientist was invited to give the first big-picture talk about autonomous control systems at the Autonomous Discovery in Science and Engineering Workshop.
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.
Using existing fish processing plants, kelp and fish waste can be converted to a diesel-like fuel to power generators or fishing boats in remote, coastal Alaska.
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.
The U.S. Department of Energy has selected the Scalable Predictive Methods for Excitations and Correlated Phenomena project to receive funding to develop software for chemical research.
