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Computer Models Show Role of Climate Change in Intense 2020 Hurricane Season
A computer modeling study of the historic 2020 North Atlantic hurricane season found that human-induced climate change played a major role. Read More »
San Francisco and Berkeley Lab Team Up on Pioneering Climate Study
Berkeley Lab computational resources are helping the City and County of San Francisco adapt to the Bay Area's changing climate and the extreme storms it is expected to bring. Read More »
Osni Marques Takes on New Role with Exascale Computing Project
Osni Marques, a staff scientist in the Applied Math and Computational Research Division, has been tapped to lead the Training & Productivity effort within the Exascale Computing Project. Read More »
Computational Analysis Enables Breakthrough in Biomolecular Dynamics
A new study with data analyses from Berkeley Lab computational researchers helps broaden the physical understanding of biomolecular assembly. Read More »
CCSE’s Hannah Klion Selected for 2022 Rising Stars
Hannah Klion, a postdoctoral researcher in the Center for Computational Sciences and Engineering, has been selected to be part of this year's Rising Stars conference. Read More »
Lindsay Bassman Awarded Prestigious Marie Curie Fellowship
AMCR’s Lindsay Bassman has been awarded one of Europe’s most competitive and prestigious postdoctoral fellowships — the Marie Skłodowska-Curie Actions — to continue her work in quantum thermodynamics. Read More »
Advancing New Battery Design with Deep Learning
A team of researchers from Berkeley Lab and UC Irvine has developed deep-learning algorithms designed to automate the quality control and assessment of new battery designs for electric cars. Read More »
Michael Mahoney Tapped to Lead the Machine Learning and Analytics Group
Berkeley Lab’s Scientific Data Division recently appointed Michael Mahoney to lead its Machine Learning and Analytics Group. Read More »
Berkeley Lab Computing Resources Enable Deeper Understanding of Supernovae Explosions
An international research team recently made history by recording the earliest post-explosion detection of a Type Ia supernova, using cosmological models developed at Berkeley Lab and supercomputing resources at NERSC. Read More »
HYPPO: Leveraging Prediction Uncertainty to Optimize Deep Learning Models for Science
With a growing need for optimization tools that can enhance deep learning models and their training to improve predictive capabilities and accelerate time-consuming computer simulations, a Berkeley Lab team developed HYPPO, an open-source software tool for hyperparameter optimization of deep neural networks. Read More »
