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Daniel Jacobson, distinguished research scientist in the Biosciences Division at ORNL, has been elected a Fellow of the American Institute for Medical and Biological Engineering, or AIMBE, for his achievements in computational biology.
Dave Weston studies how microorganisms influence plant health and stress tolerance, using the Advanced Plant Phenotyping Laboratory to accelerate research on plant-microbe interactions and develop resilient crops for advanced fuels, chemicals and materials.

ORNL took part in the “1,000 Scientists AI Jam Session,” a first-of-its-kind virtual event that brought together leading scientists from nine national laboratories to test generative artificial intelligence models for their functionality in scientific research.

During his first visit to 91, Energy Secretary Chris Wright compared the urgency of the Lab’s World War II beginnings to today’s global race to lead in artificial intelligence, calling for a “Manhattan Project 2.”

A workshop led by scientists at ORNL sketched a road map toward a longtime goal: development of autonomous, or self-driving, next-generation research laboratories.

Not only did ORNL take home top honors at the 2024 International Conference for High Performance Computing, Networking, Storage, and Analysis (SC24), but the lab’s computing staff also shared career advice and expertise with students eager to enter the world of supercomputing.

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.”

Phong Le is a computational hydrologist at ORNL who is putting his skills in hydrology, numerical modeling, machine learning and high-performance computing to work quantifying water-related risks for humans and the environment.

FREDA is a new tool being developed at ORNL that will accelerate the design and testing of next-generation fusion devices. It is the first tool of its kind to combine plasma and engineering modeling capabilities and utilize high performance computing resources.