John Lagergren, a staff scientist in Oak Ridge National Laboratory’s Plant Systems Biology group, is using his expertise in applied math and machine learning to develop neural networks to quickly analyze the vast amounts of data on plant traits amassed at ORNL’s Advanced Plant Phenotyping Laboratory.
Groundwater withdrawals are expected to peak in about one-third of the world’s basins by 2050, potentially triggering significant trade and agriculture shifts, a new analysis finds.
To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance.
Walters is working with a team of geographers, linguists, economists, data scientists and software engineers to apply cultural knowledge and patterns to open-source data in an effort to document and report patterns of human movement through previously unstudied spaces.
In the search for ways to fight methylmercury in global waterways, scientists at Oak Ridge National Laboratory discovered that some forms of phytoplankton are good at degrading the potent neurotoxin.
After completing a bachelor’s degree in biology, Toya Beiswenger didn’t intend to go into forensics. But almost two decades later, the nuclear security scientist at ORNL has found a way to appreciate the art of nuclear forensics.
When reading the novel Jurassic Park as a teenager, Jerry Parks found the passages about gene sequencing and supercomputers fascinating, but never imagined he might someday pursue such futuristic-sounding science.
At the National Center for Computational Sciences, Ashley Barker enjoys one of the least complicated–sounding job titles at ORNL: section head of operations. But within that seemingly ordinary designation lurks a multitude of demanding roles as she oversees the complete user experience for NCCS computer systems.
Having lived on three continents spanning the world’s four hemispheres, Philipe Ambrozio Dias understands the difficulties of moving to a new place.
A multi-lab research team led by ORNL's Paul Kent is developing a computer application called QMCPACK to enable precise and reliable predictions of the fundamental properties of materials critical in energy research.