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Hopper Fellow Applies Innovation, Collaboration to Foundation Models

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Software Highlight: Fiats Brings Deep Learning to Fortran at Supercomputer Scale

An ASCII art representation of the word 'FIATS' created using various keyboard characters, with the text 'Fiats: Functional Inference and Training for Surrogates' displayed below. The design emphasizes a playful and creative approach to presenting the name of the software framework focused on functional inference and training.

Software Highlight: Julienne and Assert Strengthen Fortran Code Reliability

Software Highlight: Caffeine Supercharges Fortran for the Exascale Era

A graphic representation of a coffee cup with layered components illustrating a software architecture. The layers from top to bottom include: 'Compiled Application,' 'PRIF,' 'Caffeine,' and 'GASNet-EX.' The base of the cup is labeled 'System Runtime & Memory Technologies,' indicating the foundational technologies that support the application.

Berkeley Researchers Receive IEEE Best Paper Award for Exascale Software Insights

The image features two individuals side by side. On the left, a person with shoulder-length dark hair is wearing a black, textured top and stands in front of a whiteboard filled with scientific equations and diagrams. On the right, another person with a shaved head is wearing a gray hoodie. The background appears to be a dimly lit environment, possibly an indoor space.

New ESnet Report Looks at AI-Powered Future for Scientific Networking

Illustration ESnet AI Rendered

ASU and Berkeley Lab tools make AI safer for science

Neutrino Flavors Encoded, a Leap for Quantum Computing and Physics

Expert Interview: Sean Peisert on Cybersecurity Research

Deep Learning School Convenes AI Experts, Researchers

Berkeley Researchers Advance Fluid Flow Modeling for Better Energy Production

Simulation showing a 3D model of fluid flow through fractured subsurface dolomite, with color indicating different regions or properties within the flow.

Machine Learning Breakthrough Transforms Battery Lifespan Prediction

Illustration showing a machine learning framework for battery testing: a Gaussian Process model visualizes battery energy predictions across cycles and parameters, with model entropy highlighting areas of uncertainty for new experiments; additional graphics show cell testing results, predicted energy decline, and a gauge indicating battery C-rate from low to high.
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