PNNL computational scientist Diana Bacon’s role as carbon storage associate editor uses her expertise in subsurface modeling and quantitative risk assessment.

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

PNNL provided ultra-low measurements of argon-39 to date groundwater as part of a collaborative study of the aquifer in California’s San Joaquin Valley. PNNL is one of only a few laboratories worldwide with this capability.

Machine learning techniques are accelerating the development of stronger alloys for power plants, which will yield efficiency, cost, and decarbonization benefits.

PNNL is lending analytics expertise to a new first-of-its-kind electric-vehicle carsharing program being launched in Hood River, Oregon.

Svitlana Volkova, chief scientist for decision intelligence and analytics at PNNL, was invited as a panelist at the SIAM International Conference on Data Mining

Principles derived from coastal wetlands to describe wetland channel cross-sections were applicable to the Columbia River estuary, but not the tidal river.

Aaron Luttman, a mathematician and senior technical advisor at PNNL, was invited as a guest speaker during the 2021 National Math Festival.

Researchers used deep learning methods to estimate the subsurface permeability of a watershed from stream discharge measurements.

PNNL data scientists Svitlana Volkova and Emily Saldanha, along with former PNNL intern Pamela Bilo Thomas, will publish their research on online information spread in Nature's Scientific Reports.

High school students from across Washington State competed in the Pacific Northwest Regional Science Bowl, hosted online by PNNL, for a chance to advance to the national competition in May.

Understanding the Earth as a complex, dynamic system includes investigating what happens on, above and below its surface.

A team of researchers from 10 national laboratories and eight universities is conducting hydraulic shearing tests to explore the potential for geothermal energy at the Sanford Underground Research Facility (SURF).

An updated version of a widely used database will provide powerful insights into the global carbon cycle.

Michael Henry, a senior data scientist at PNNL, has accepted a joint appointment at the Texas A&M University RELLIS Center for Applied Research and Experiential Learning.

PNNL data scientists Henry Kvinge and Ted Fujimoto presented their research on few-shot learning and reinforcement learning, respectively, at workshops during the 2021 AAAI Conference on Artificial Intelligence.

Variability in field measurement conditions likely does not impact estimates of an important flux in the global carbon cycle.

As a member of the NAM board of directors, Brett Jefferson, PNNL data scientist, will help lead the professional association’s mission to advance mathematical excellence of underrepresented minorities.

New Distinguished Graduate Research Program will provide opportunities for North Carolina State University doctoral students to tackle real-world data science challenges alongside PNNL scientists.

New approach allows nonexperts to optimize control of a building's energy systems without adding computing power or proprietary software.