Share
Berkeley Lab scientists have developed a faster method for topological data analysis, improving efficiency in uncovering hidden structures in complex datasets. This breakthrough could transform data analysis in fields like machine learning and biology.
Berkeley Lab researchers developed an AI algorithm to optimize ion beams at RHIC, enhancing collision precision and advancing insights into atomic structure and the Universe’s origins.
FerroX, a new open-source, 3D simulation framework, could advance record-breaking energy efficiency in microelectronics.
Berkeley Lab’s EScAIP is a machine-learning method that accelerates atomistic simulations, reducing memory usage by more than fivefold and boosting speed by over tenfold.
A leading expert in quantum computing, Bert de Jong discusses why quantum computing matters, where it is today, and the potential of this emerging technology to change science.
Berkeley Lab scientists have developed a faster method for topological data analysis, improving efficiency in uncovering hidden structures in complex datasets. This breakthrough could transform data analysis in fields like machine learning and biology.
A collaboration among Berkeley Lab researchers and others has led to the development of ML models that enable real-time predictions of particle heating in fusion plasmas, potentially transforming how researchers conduct physics experiments.
Stefan Wild has been named a 2025 SIAM Fellow. This recognition celebrates his groundbreaking work in derivative-free optimization, which has made significant impacts across multiple scientific and engineering disciplines.
By becoming an institutional member of HPSF, Berkeley Lab is reinforcing its commitment to scientific software stewardship and fostering collaborations that shape the future of high-performance computing.
Each year, the Computing Sciences Area Postdoc Symposium provides a platform for early-career researchers to showcase their cutting-edge work while honing their communication skills. With mentorship, training, and an engaged audience, the event helps postdocs refine their presentations and build confidence for the next steps in their careers.
Berkeley Lab researchers have developed EScAIP, a groundbreaking machine-learning method that accelerates atomistic simulations by improving model scalability. This approach reduces memory usage by more than fivefold and delivers results over ten times faster than current models.
SciData transforms data-driven discovery and understanding through the development and application of novel data science methods, technologies, and infrastructures with scientific partners.
The Applied Mathematics & Computational Research Division conducts research and development in mathematical modeling and simulation, algorithm design, data storage, management and analysis, computer system architecture, and high-performance software implementation.
The Center for Advanced Mathematics for Energy Research Applications (CAMERA) is an integrated, cross-disciplinary center aimed at inventing, developing, and delivering the fundamental new mathematics required to capitalize on experimental investigations at scientific facilities.
The Quantum Systems Accelerator is catalyzing national leadership in quantum information science to co-design the algorithms, quantum devices, and engineering solutions needed to deliver certified quantum advantage in Department of Energy scientific applications. Berkeley Lab leads the center. Sandia National Laboratories is the lead partner.
