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Matt McCarthy uses images collected from the sky to interpret changes to the coastlines and oceans for national security research. Credit: Carlos Jones and Rachel Green/ORNL, U.S. Dept. of Energy

When Matt McCarthy saw an opportunity for a young career scientist to influence public policy, he eagerly raised his hand.

Sophie Voisin, an ORNL software engineer, was part of a team that won a 2014 R&D 100 Award for work on Intelligent Software for a Personalized Modeling of Expert Opinions, Decisions and Errors in Visual Examination Tasks. Credit: Jason Richards/ORNL, U.S. Dept. of Energy

Cameras see the world differently than humans. Resolution, equipment, lighting, distance and atmospheric conditions can impact how a person interprets objects on a photo.

ORNL identity science researcher Nell Barber works on a facial recognition camera. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

Though Nell Barber wasn’t sure what her future held after graduating with a bachelor’s degree in psychology, she now uses her interest in human behavior to design systems that leverage machine learning algorithms to identify faces in a crowd.

LandScan Global depicts population distribution estimates across the planet. The darker orange and red colors above indicate higher population density. Credit: ORNL, U.S. Dept. of Energy

It’s a simple premise: To truly improve the health, safety, and security of human beings, you must first understand where those individuals are.

The ORNL researchers’ findings may enable better detection of uranium tetrafluoride hydrate, a little-studied byproduct of the nuclear fuel cycle, and better understanding of how environmental conditions influence the chemical behavior of fuel cycle materials. Credit: Kevin Pastoor/Colorado School of Mines

ORNL researchers used the nation’s fastest supercomputer to map the molecular vibrations of an important but little-studied uranium compound produced during the nuclear fuel cycle for results that could lead to a cleaner, safer world.

ORNL biogeochemist Teri O’Meara is focused on improving how coastal systems are represented in global climate models, enabling better predictions about the future of these critical ecosystems. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Surrounded by the mountains of landlocked Tennessee, Oak Ridge National Laboratory’s Teri O’Meara is focused on understanding the future of the vitally important ecosystems lining the nation’s coasts.

The Energy Exascale Earth System Model project reliably simulates aspects of earth system variability and projects decadal changes that will critically impact the U.S. energy sector in the future. A new version of the model delivers twice the performance of its predecessor. Credit: E3SM, Dept. of Energy

A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.

U.S. Secretary of Energy Granholm tours ORNL’s world-class science facilities

Energy Secretary Jennifer Granholm visited ORNL on Nov. 22 for a two-hour tour, meeting top scientists and engineers as they highlighted projects and world-leading capabilities that address some of the country’s most complex research and technical challenges. 

By studying the activity patterns of populations around the world, scientists at ORNL are identifying the communities that are most likely to face extreme climate events and associated national security challenges. Credit: Erik Schmidt/ORNL, U.S. Dept. of Energy

Using novel data sets and computing systems, researchers at ORNL are simulating how climate change affects the safety and security of the country. 

Former ORNL Director Thom Mason presents Tom Kollie with a National Intelligence Meritorious Unit Citation on behalf of James Clapper, former director of national intelligence, and the national intelligence community in June 2017. Credit: Jason Richards/ORNL, U.S. Dept. of Energy

A 25-year career with the U.S. Navy, commanding combat missions overseas, brought Tom Kollie back to where he came from — ready to serve his country in a new way.