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Three graphs and one map with grey, long shapes showing the single block group.

Joe Tuccillo, a human geography research scientist, leads the UrbanPop project that uses census data to create synthetic populations. Using a Python software suite called Likeness on ORNL’s high-performance computers, Tuccillo’s team generates a population with individual ‘agents’ designed to represent people that interact with other agents, facilities and services in a simulated neighborhood. 

Group of over 20 participants, both girls and boys, line up in a group with four rows of 13 in the quad - outside area of ORNL.

ORNL hosted the Mid-South Regional Chapter of the American Society for Photogrammetry and Remote Sensing, or ASPRS. Participants spanning government, academia and industry engaged in talks, poster sessions, events and workshops to further scientific discovery in a field devoted to using pictures to understand changes to the earth’s inhabitants and landscape. 

This photo is of three men sitting around a laptop computer that happens to be working on cybersecurity testing equipment.

A newly established internship between ORNL and Maryville College is bringing cybersecurity careers to a local liberal arts college. The internship was established by a Maryville College alumni who recently joined ORNL. 

Arial view of the Atchafalaya Basin

In the wet, muddy places where America’s rivers and lands meet the sea, scientists from the Department of Energy’s Oak Ridge National Laboratory are unearthing clues to better understand how these vital landscapes are evolving under climate change.

Man in blue suit and blue and white button down with brown air and brown facial hair smiles for a photo with a green and teal background. Plus a quote

As a data scientist, Daniel Adams uses storytelling to parse through a large amount of information to determine which elements are most important, paring down the data to result in the most efficient and accurate data set possible.

Redish orange sample of material, round in size and small (taking up only a quarter of the image). There is a dark grey floor and blue light background

Despite strong regulations and robust international safeguards, authorities routinely interdict nuclear materials outside of regulatory control. Researchers at ORNL are exploring a new method that would give authorities the ability to analyze intercepted nuclear material and determine where it originated. 

Man in blue shirt and grey pants holds laptop and poses next to a green plant in a lab.

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.

Caption: Participants gather for a group photo after discussing securing AI systems for critical national security data and applications.  Photo by Liz Neunsinger/ORNL, U.S. Dept. of Energy

Researchers at the Department of Energy’s Oak Ridge National Laboratory met recently at an AI Summit to better understand threats surrounding artificial intelligence. The event was part of ORNL’s mission to shape the future of safe and secure AI systems charged with our nation’s most precious data. 

Mohamad Zineddin

Mohamad Zineddin hopes to establish an interdisciplinary center of excellence for nuclear security at ORNL, combining critical infrastructure assessment and protection, risk mitigation, leadership in nuclear security, education and training, nuclear security culture and resilience strategies and techniques.

Joon-Seok Kim Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

Researchers at ORNL are using a machine-learning model to answer ‘what if’ questions stemming from major events that impact large numbers of people. By simulating an event, such as extreme weather, researchers can see how people might respond to adverse situations, and those outcomes can be used to improve emergency planning.