Topic: Data Science

LLNL will collaborate with Machina Labs to apply ML to aluminum sheet metal processing for aerospace and automotive applications. Five recently announced LLNL-led projects will be funded by HPC4EI.

fpzip is a library for lossless or lossy compression of multidimensional floating-point arrays. It was primarily designed for lossless compression.

High-precision numerical data from computer simulations, observations, and experiments is often represented in floating point and can easily reach terabytes to petabytes of storage.

Nisha Mulakken is advancing COVID-19 R&D and mentoring the next generation. “The opportunities we are exposed to early in our careers can shape the limits we place on ourselves and our approaches to challenges we encounter throughout our careers,” she says.

LLNL’s Computing Directorate heads to the 32nd annual Supercomputing Conference (SC20) held virtually on November 9–19. Although the format is different this year, we’re turning out in full force.

LLNL participates in the 32nd annual Supercomputing Conference (SC20) held virtually on November 9–19, 2020.

LLNL has installed a new AI accelerator into the Corona supercomputer, allowing researchers to run simulations while offloading AI calculations from those simulations to the AI system.

Livermore Computing's CTO Bronis de Supinski discusses the Lab's early work with, and vision for, the Cerebras-Lassen hardware integration.

CASC researcher Harsh Bhatia thrives in the Lab’s versatile research environment. “At the Lab, no two problems are the same. Therefore, as a team, researchers deliver hundreds of new data science solutions each year. We are very fortunate to have access to many high-impact projects so we can really make a difference with our data science or data analysis solutions," he says.

This summer, the Computing Scholar Program welcomed 160 undergraduate and graduate students into virtual internships. The Lab’s open-source community was already primed for student participation.

Computing’s summer hackathon was held virtually on August 6–7 and featured presentations from teams who tested software technologies, expanded project features, or explored new ways of analyzing data.

Computing’s fourth annual Developer Day was held as a virtual event on July 30 with 8 speakers and 90 participants.

LLNL and Cerebras Systems have installed the company’s AI computer into Lassen, making LLNL the first institution to integrate the cutting-edge AI platform with a supercomputer.

This video provides an overview of LLNL projects in which data scientists work with domain scientists to address major challenges in healthcare.

Ian Karlin on AI hardware integration into HPC systems, workflows, followed by a talk about software integration of AI accelerators in HPC with Brian Van Essen.

Lawrence Livermore National Lab has named Stefanie Guenther as Computing’s fourth Sidney Fernbach Postdoctoral Fellow in the Computing Sciences. This highly competitive fellowship is named after LLNL’s former Director of Computation and is awarded to exceptional candidates who demonstrate the potential for significant achievements in computational mathematics, computer science, data science, or scientific computing.

Two papers featuring LLNL scientists were accepted in the 2020 International Conference on Machine Learning (ICML), one of the world’s premier conferences of its kind.

In this year's Data Science Challenge with UC Merced, 21 students developed machine learning models capable of differentiating potentially explosive materials from other types of molecules.

Surrogate models supported by neural networks could lead to new insights in complicated physics problems such as inertial confinement fusion.

Highlights include response to the COVID-19 pandemic, high-order matrix-free algorithms, and managing memory spaces.

A team led by an LLNL computer scientist proposes a deep learning approach aimed at improving the reliability of classifier models for predicting disease types from diagnostic images.

LLNL scientists have taken a step forward in the design of future materials with improved performance by analyzing its microstructure using artificial intelligence.

To help accelerate discovery of therapeutic antibodies or antiviral drugs for SARS-CoV-2, LLNL has launched a searchable data portal to share its COVID-19 research with scientists and the public.

LLNL's Jay Thiagarajan joins the Data Skeptic podcast to discuss his recent paper "Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models." The episode runs 35:50.

An LLNL team developed ML tools that extract and structure information from the text and figures of nanomaterials articles using NLP, image analysis, computer vision, and visualization techniques.