The Scalable Biomedical Modeling Group at Oak Ridge National Laboratory develops methods and tools that make biomedical and health data usable, reliable, and impactful at scale. The group is motivated by a rapidly changing scientific landscape, wherein advances in AI are reshaping research workflows, from hypothesis generation and experimental design to automated code execution, model interpretation, and communication of results. The group’s mission is to create, validate, and deploy these capabilities to accelerate data-driven discovery while safeguarding privacy and upholding scientific rigor.
Group research focuses on three key areas:
Data Curation and AI Readiness
Tackling the growing complexity of distributed and sensitive datasets by building pipelines for curation, harmonization, and interoperability — Group members work to make biomedical data AI-ready and to lower the barriers to responsible, collaborative science.
Privacy-Enhancing Technologies for AI
Developing and evaluating approaches such as synthetic data generation, federated learning, and adversarial privacy testing — The group aims to ensure that sensitive biomedical and proprietary scientific data can be shared, analyzed, and modeled while preserving utility, integrity, and trust.
High-Performance Computing and Agentic AI Workflows
Harnessing leadership-class computing platforms and emerging AI agent technologies to design, train, and deploy models at scale — The group is creating workflows that link simulation, learning, and decision-making to enable more autonomous and adaptive scientific exploration.
Although their research centers on biomedical and public health applications, the group members also address pressing challenges in biological and environmental sciences, energy, and national security. The group aims to deliver both foundational advances (e.g., new algorithms, benchmark frameworks, and privacy-preserving methods) and practical solutions that can be deployed on the U.S. Department of Energy’s world-leading computing resources.
