Topic: Data Science

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.

In this video from the Stanford HPC Conference, Katie Lewis presents "The Incorporation of Machine Learning into Scientific Simulations at Lawrence Livermore National Laboratory."

LLNL researchers have identified an initial set of therapeutic antibody sequences, designed in a few weeks using machine learning and supercomputing, aimed at binding and neutralizing SARS-CoV-2.

On the Hidden in Plain Sight podcast, LLNL director Bill Goldstein explains how the Lab crunches data to shape the future.

Held in early March, the WiDS Livermore event featured four technical talks from LLNL data scientists.

The early-March event was the third annual WiDS Livermore event, featuring speakers, a career panel, mentoring, and a livestream.

LLNL has infrastructure, unique research capabilities, and a dedicated team of scientists and engineers supporting the fight against COVID-19.

LLNL scientists are contributing to the global fight against COVID-19 by combining AI/ML, bioinformatics, and supercomputing to help discover candidates for new antibodies and pharmaceutical drugs.

The university and LLNL lay the groundwork for a direct pipeline between the two, opening a door to research collaborations as well as job and internship opportunities for students and alumni.

Rafael Rivera-Soto is passionate about artificial intelligence, deep learning, and machine learning technologies. He works in LLNL’s Global Security Computing Applications Division, also known as GSCAD.