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Tag: SciData

Lavanya Ramakrishnan Presents at JSSPP and ESSA

Computer scientists Gonzalo Rodrigo Alvarez and Lanvanya Ramakrishnan of Computational Research Division have released software that automates a number of steps when scientists run workflows on HPC systems. The software helps scientists make more efficient use of their time and computing resources. Photographed at NERSC, Lawrence Berkeley National Laboratory on Monday, December 4, 2017 in Berkeley, Calif. 12/04/17

Explaining Software Security in Scientific Computing

Sean Peisert, senior scientist at Berkeley Lab (Credit: Berkeley Lab)

Sean Peisert Tapped to Take on Deputy Director Role at Trusted CI

Sean Peisert

E. Wes Bethel Retires After 32 Years at Berkeley Lab

Portrait E Wes Bethel

Data Management Platform Strengthens Wildland Fire Science

A firefighter uses a drip torch to ignite a prescribed fire. This type of wildland fire is used to reduce the buildup of materials that fuel wildfires. (Credit: U.S. Department of Energy)

Summer Researchers Return In-Person, Remotely

A 2018 summer researcher presents a poster to Berkeley Lab Scientist John Wu. (Credit: Berkeley Lab)

New Encoder-Decoder Overcomes Limitations in Scientific Machine Learning

Figure: A general depiction of the encoder-decoder framework with an integrated CRF-RNN layer. The input data is fed into a chosen encoder, which then is upsampled by the U-Net-based decoder. Both the decoder output and the initial image serve as inputs for the CRF-RNN layer, which produces the final pixel-based prediction. (Credit: M. Avaylon, T. Perciano, Z. Bai)

New Book Addresses the Unique Artificial Intelligence Needs of High Energy Physics

A 3d display of a simulated High Luminosity LHC collision event as seen by the ATLAS inner tracking detector (ITk). Image: Atlas Collaboration.

Computing Sciences Staff to Share Expertise at 2022 ECP Meeting

Exascale Computing Project

Computational Analysis Enables Breakthrough in Biomolecular Dynamics

Schematic of angular states and HS-AFM snapshots of protein nanorods in their energetically preferred orientations, corresponding to specific directions of the mineral lattice. Orientational free energy landscape and heat map of relative populations at each angle determined from deep learning analysis of HS-AFM data. (Credit: Stephane A. King, PNNL)

Michael Mahoney Tapped to Lead the Machine Learning and Analytics Group

Postdocs Present Wide-Ranging Research at 2022 Symposium

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