Data Science in the News

Did you know we have a monthly newsletter? View past volumes and subscribe.

Ana Kupresanin featured in FOE alumni spotlight

March 10, 2021 - 
LLNL's Ana Kupresanin, deputy director of the Center for Applied Scientific Computing and member of the Data Science Institute council, was recently featured in a Frontiers of Engineering (FOE) alumni spotlight. Kupresanin develops statistical and machine learning models that incorporate real-world variability and probabilistic behavior to quantify uncertainties in engineering and physics...

Lab researchers explore ‘learn-by-calibration’ approach to deep learning to accurately emulate scientific process

Feb. 10, 2021 - 
An LLNL team has developed a “Learn-by-Calibrating” method for creating powerful scientific emulators that could be used as proxies for far more computationally intensive simulators. Researchers found the approach results in high-quality predictive models that are closer to real-world data and better calibrated than previous state-of-the-art methods. The LbC approach is based on interval...

From intern to mentor, Nisha Mulakken builds a career in bioinformatics

Nov. 3, 2020 - 
The COVID-19 pandemic has sparked a wave of new research and development at the Lab, and Nisha Mulakken is very busy. The biostatistician has enhanced the Lawrence Livermore Microbial Detection Array (LLMDA) system with detection capability for all variants of SARS-CoV-2. The technology detects a broad range of organisms—viruses, bacteria, archaea, protozoa, and fungi—and has demonstrated...

Advancing healthcare with data science (VIDEO)

Aug. 3, 2020 - 
This video provides an overview of projects in which data scientists work with domain scientists to address major challenges in healthcare. To help fight the COVID-19 pandemic, researchers are developing computer models to search for potential antibody and antiviral drug treatments, sharing a data portal with scientists and the general public, and analyzing drug compounds via a novel text...

Machine learning model may perfect 3D nanoprinting

July 29, 2020 - 
Two-photon lithography (TPL)—a widely used 3D nanoprinting technique that uses laser light to create 3D objects—has shown promise in research applications but has yet to achieve widespread industry acceptance due to limitations on large-scale part production and time-intensive setup. LLNL scientists and collaborators turned to machine learning to address two key barriers to industrialization...

Lockdown doesn’t hinder annual Data Science Challenge

June 26, 2020 - 
Due to the COVID-19 pandemic and shelter-in-place restrictions, this year’s Data Science Challenge with the University of California, Merced was an all-virtual offering. The two-week challenge involved 21 UC Merced students who worked from their homes through video conferencing and chat programs to develop machine learning models capable of differentiating potentially explosive materials from...

Modeling neuronal cultures on 'brain-on-a-chip' devices

June 12, 2020 - 
For the past several years, LLNL scientists and engineers have made significant progress in development of a three-dimensional “brain-on-a-chip” device capable of recording neural activity of human brain cell cultures grown outside the body. The team has developed a statistical model for analyzing the structures of neuronal networks that form among brain cells seeded on in vitro brain-on-a...

Lab team studies calibrated AI and deep learning models to more reliably diagnose and treat disease

May 29, 2020 - 
A team led by LLNL computer scientist Jay Thiagarajan has developed a new approach for improving the reliability of artificial intelligence and deep learning-based models used for critical applications, such as health care. Thiagarajan recently applied the method to study chest X-ray images of patients diagnosed with COVID-19, arising due to the novel SARS-Cov-2 coronavirus. Read more at LLNL...

AI identifies change in microstructure in aging materials

May 26, 2020 - 
LLNL scientists have taken a step forward in the design of future materials with improved performance by analyzing its microstructure using AI. The work recently appeared online in the journal Computational Materials Science. Read more at LLNL News.

COVID-19 research goes public through new portal

May 18, 2020 - 
A new online data portal is making available to the public a wealth of data LLNL scientists have gathered from their ongoing COVID-19 molecular design projects, particularly the computer-based “virtual” screening of small molecules and designed antibodies for interactions with the SARS-CoV-2 virus for drug design purposes. The portal houses a wealth of data LLNL scientists have gathered from...

Interpretable AI in healthcare (PODCAST)

May 17, 2020 - 
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. Listen at Data Skeptic.

The incorporation of machine learning into scientific simulations at LLNL (VIDEO)

May 5, 2020 - 
In this video from the Stanford HPC Conference, Katie Lewis presents "The Incorporation of Machine Learning into Scientific Simulations at Lawrence Livermore National Laboratory." Read more and watch the video at insideHPC.

Building knowledge and insights using machine learning of scientific articles

May 5, 2020 - 
Nanomaterials are widely used at LLNL and in industry for many applications from catalysis to optics to additive manufacturing. The combination of nanomaterials’ shape, size, and composition can impart unique optical, electrical, mechanical, or catalytic properties needed for a specific application. However, synthesizing a specific nanomaterial and scaling up its production is often...

New partnership results in increased access to compelling 'real world data'

April 21, 2020 - 
Through a new partnership between the UC San Diego Library, Halıcıoğlu Data Science Institute (HDSI), and LLNL's Data Science Institute, UCSD library patrons can now access and analyze two new “real world” data sets from LLNL. The Open Data Initiative collection shares some of LLNL’s challenging and unique data sets, which range in complexity from large-scale, domain-specific simulated data...

Using data to build a secure future (PODCAST)

April 21, 2020 - 
On the Hidden in Plain Sight podcast, LLNL director Bill Goldstein explains how the Lab crunches data to shape the future. Listen at Mission.org.

Local Women in Data Science conference showcases Lab research

April 3, 2020 - 
For the third consecutive year, LLNL hosted a Women in Data Science (WiDS) regional event on March 2. The event drew dozens of attendees from LLNL, Sandia National Laboratories, local universities, and Bay Area commercial companies. Livermore was one of over 200 regional events in 60 countries coordinated with the main WiDS conference at Stanford University. According to the WiDS website...

LLNL creates web resources to aid in fight against COVID-19

March 30, 2020 - 
LLNL is fully committed to helping protect the U.S. from COVID-19 and to speed the recovery of those affected. As a world-class research institute, we have considerable infrastructure, unique research capabilities and a dedicated team of scientists and engineers supporting the fight against the COVID-19 pandemic. Our current COVID-19 research and response activities are focused on four broad...

Lab antibody, anti-viral research aids COVID-19 response

March 26, 2020 - 
LLNL scientists are contributing to the global fight against COVID-19 by combining artificial intelligence/machine learning, bioinformatics and supercomputing to help discover candidates for new antibodies and pharmaceutical drugs to combat the disease. Armed with the virus’ predicted 3D structure and a few antibodies known to bind and neutralize SARS, an LLNL team led by Daniel Faissol and...

DSI sponsors LLNL hackathon

Feb. 18, 2020 - 
Since 2012, Lawrence Livermore National Laboratory’s (LLNL’s) Computing directorate has held hackathons three times a year. These spring, summer, and fall events are scheduled over 24 hours and invite software teams to work on new ideas, programming languages, open-source tools, or project tasks. Exploration and experimentation are highly encouraged, and “It’s OK to fail” is the event mantra...

Machine learning accelerates high-performance materials development

Feb. 13, 2020 - 
Lawrence Livermore National Laboratory (LLNL) and its partners rely on timely development and deployment of diverse materials to support a variety of national security missions. However, materials development and deployment can take many years from initial discovery of a new material to deployment at scale. Now, an interdisciplinary team of LLNL researchers from the Physical and Life Sciences...