Groups

The AI & Learning Systems (AILearn) Group builds the foundations of AI for Science—models, methods, and systems that fuse scientific priors with modern ML.

As a multi-disciplinary center, the Computational Cosmology Center (C3) is bringing together astrophysicists and computational scientists whose goals are to develop the methods, tools, and technologies to meet the scientific challenges of modern cosmology.

The Computational Biosciences Group (CBG)’s work is at the intersection of computing and biosciences and innovates data analytics, data management, and statistical machine learning methods for experimental data and mechanistic models towards addressing our nation’s energy, environment, and health needs.

The Integrated AI-ready Data Systems (IADS) Group is focused on data integration and frameworks for computational and data analysis systems. Our approach is distinguished by a strong focus on partnerships and collaborations with scientific user communities.

The Physics and X-ray Computing Group (PHAX) is committed to advancing experimental physics by offering innovative hardware and software solutions. In collaboration with domain experts, we develop and support application frameworks, tools, ML models, and algorithms used by thousands of scientists.

The Scientific Data Management & Usable Data Systems (SDM-UDS) Group enables and accelerates scientific discoveries through AI-powered data management, automated data integration, and ML-driven analysis tools and libraries. We also research and develop AI-assisted methods and corresponding software to improve the usability, quality, and security of scientific computing, including automated metadata extraction,  algebraic optimization,  robust data governance, and secure, reproducible AI/ML workflows.

By constantly improving the practices and knowledge around stewardship of software, the Scientific Software Engineering (SSE) Group aims to create a broad and supportive community of researchers and engineers performing state-of-the-art software development for all aspects of publicly funded science.