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ORNL R&D data scientist Max Pasini is posing for a portrait with a blue background, black button up long sleeve shirt

Massimiliano (Max) Lupo Pasini, an R&D data scientist from ORNL, was awarded the National Energy Research Scientific Computing Center’s High Performance Computing Achievement Award for High Impact Scientific Achievement for his work in “Groundbreaking contributions to scientific machine learning, particularly through the development of HydraGNN.”

Image is blue and green with the background being a building on the left, merging into the photo on the right which are pictures of doppler radar graphics

Researchers at the Department of Energy’s Oak Ridge National Laboratory are using non-weather data from the nationwide weather radar network to understand how to track non-meteorological events moving through the air for better emergency response. 

Man is flying drone in hurricane aftermath, holding the controller

During Hurricanes Helene and Milton, ORNL deployed drone teams and the Mapster platform to gather and share geospatial data, aiding recovery and damage assessments. ORNL's EAGLE-I platform tracked utility outages, helping prioritize recovery efforts. Drone data will train machine learning models for faster damage detection in future disasters. 

Supriya Chinthavali is standing with the Summit supercomputer at ORNL

The Department of Energy’s Office of Electricity, in partnership with ORNL, has launched an experimental platform for energy sector-related data with enhanced emphasis on governance and usability. 

A large group of attendees are pictured outside of Jackson Center in Huntsville, Alabama

ORNL and NASA co-hosted the fourth iteration of this invitation-only event, which brings together geospatial, computational, data and engineering experts around a theme. This year’s gathering focused on how artificial intelligence foundation models can enable geospatial digital twins. 

This is a simulated image of the project to build a new network that artificial intelligence and machine learning to steer experiments and analyze data faster and more accurately. will enable

To bridge the gap between experimental facilities and supercomputers, experts from SLAC National Accelerator Laboratory are teaming up with other DOE national laboratories to build a new data streaming pipeline. The pipeline will allow researchers to send their data to the nation’s leading computing centers for analysis in real time even as their experiments are taking place. 

This illustration demonstrates how atomic configurations with an equiatomic concentration of niobium (Nb), tantalum (Ta) and vanadium (V) can become disordered. The AI model helps researchers identify potential atomic configurations that can be used as shielding for housing fusion applications in a nuclear reactor. Credit: Massimiliano Lupo Pasini/ORNL, U.S. Dept. of Energy

A study led by the Department of Energy’s Oak Ridge National Laboratory details how artificial intelligence researchers created an AI model to help identify new alloys used as shielding for housing fusion applications components in a nuclear reactor. The findings mark a major step towards improving nuclear fusion facilities.

ORNL uses old radiation detectors repurposed into plaques

On Feb. 15, 2024, the one billionth item, also known as an “occupancy,” was scanned at the Port of Aqaba, Jordan, one of the early sites where radiation detection equipment was installed. This milestone shows the extent of countries committed to preventing the spread of radioactive material through the amount of data volunteered to ORNL for continuous improvement. As adversaries push the limits of smuggling dangerous material, this collaboration pushes back through science-backed analysis. 

Arial view of homes in a waffle style pattern

Researchers at ORNL are using satellite images of homes under construction to address gaps in census data, especially in areas like Sub-Saharan Africa. By analyzing these images, they estimate dwelling sizes and population densities where traditional data is sparse. This method improves population estimates and supports national security by enhancing emergency response capabilities.

Debjani Singh

Debjani Singh, a senior scientist at ORNL, leads the HydroSource project, which enhances hydropower research by making water data more accessible and useful. With a background in water resources, data science, and earth science, Singh applies innovative tools like AI to advance research. Her career, shaped by her early exposure to science in India, focuses on bridging research with practical applications.