Advanced Computing for Health Sciences Section 

Delivers scalable computational solutions to biomedical and healthcare delivery challenges.

The Advanced Computing for Health Sciences section harnesses ORNL computing and data expertise to accelerate breakthroughs in the health sciences. The Biostatistics and Multiscale Systems group develops statistical, machine-learning, and deep-learning methodologies for large-scale genomics, text, and imaging applications, while the Multimodal Data Analytics group leverages expertise in large-scale biomedical informatics and statistical genetics to build and use tools for healthcare needs, such as scalable AI and machine-learning solutions for multidimensional, multimodal data in HPC environments.


Advanced Computing Methods for Engineered Systems

Develops scalable and coupled algorithms for engineering, cybernetics, autonomous and complex systems applications.

Researchers in the Advanced Computing Methods for Engineered Systems section supply a wide variety of tools aimed at improving understanding of complex phenomena and the deployment of applied, large-scale systems. These include performance-portable applications and algorithms for coupled physics simulations at scale, as well as high-fidelity modeling and numerical tools for fluid dynamics and complex flow physics; disruptive technologies at the extreme scale, such as multimodal sensing and pioneering algorithms for large laser arrays, signal processing algorithms, and online computing for sensing platforms; and solutions for sensor data aggregation.


Advanced Computing Methods for Physical Sciences

Delivers multiscale, multifidelity computational models and systems developing algorithms and analytics for the physical sciences.

The Advanced Computing Methods for Physical Sciences section encompasses a wide range of physical science phenomena, including earth systems, materials, and quantum information science. Researchers in this section develop and deploy models for climate researchers at unprecedented scales, contribute to the development of Quantum Monte Carlo and Hubbard applications, deliver multiscale material models, and explore the potential of quantum computers, networks, and sensors to transform scientific discovery.


Last Updated: January 31, 2021 - 5:17 pm