Topic: Data Science

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.

An LLNL-led team proposes a DL 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 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 open 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.

New year, new hackathon! The January 30–31 event was Computing’s 23rd hackathon and the 1st scheduled in the winter season.

Laser-fusion researchers have turned to machine-learning techniques to seek the combinations of laser pulse characteristics and target design needed to optimize target implosions for ICF.

A multi-institutional consortium aims to speed up the drug discovery pipeline by building predictive, data-driven pharmaceutical models.

ADAPD integrates expertise from DOE national labs to analyze growing global data streams and traditional intelligence data, enabling early warning of nuclear proliferation activities.

Researchers develop innovative data representations and algorithms to provide faster, more efficient ways to preserve information encoded in data.

Cindy Gonzales earned a bachelor’s degree, started her master’s degree, and changed careers—all while working at the Lab. Meet one of our newest data scientists.

Highlights include perspectives on machine learning and artificial intelligence in science, data driven models, autonomous vehicle operations, and the OpenMP standard 5.0.