Data Science in the News

CASC research in machine learning robustness debuts at AAAI conference

LLNL’s Center for Applied Scientific Computing (CASC) has steadily grown its reputation in the artificial intelligence (AI)/machine learning (ML) community—a trend continued by three papers accepted at the 35th AAAI Conference on Artificial Intelligence, held virtually on February 2–9, 2021. Computer scientists Jayaraman Thiagarajan, Rushil Anirudh, Bhavya Kailkhura, and Peer-Timo Bremer led...

LLNL physicist wins Young Former Student award

Texas A&M University’s Department of Nuclear Engineering on December 10 announced it has honored LLNL physicist Kelli Humbird with its 2020-21 Young Former Student award for her work at LLNL in combining machine learning with inertial confinement fusion (ICF) research. Humbird graduated from Texas A&M with a PhD in nuclear engineering in 2019. Since joining the Laboratory as an intern in 2016...

NeurIPS papers aim to improve understanding and robustness of machine learning algorithms

The 34th Conference on Neural Information Processing Systems (NeurIPS) is featuring two papers advancing the reliability of deep learning for mission-critical applications at LLNL. The most prestigious machine learning conference in the world, NeurIPS began virtually on Dec. 6. The first paper describes a framework for understanding the effect of properties of training data on the...

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

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...

Machine learning speeds up and enhances physics calculations

Interpreting data from NIF’s cutting-edge high energy density science experiments relies on physics calculations that are so complex they can challenge LLNL supercomputers, which stand among the best in the world. A collaboration between LLNL and French researchers found a novel way to incorporate machine learning and neural networks to significantly speed up inertial confinement fusion...

Advancing healthcare with data science (VIDEO)

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

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...

LLNL papers accepted into prestigious conference

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. Read more at LLNL News.

Lockdown doesn’t hinder annual Data Science Challenge

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...

AI identifies change in microstructure in aging materials

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

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...

Building knowledge and insights using machine learning of scientific articles

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...

Lab promotes diversity, tech at Women in Data Science regional event

For the third consecutive year, Lawrence Livermore National Laboratory (LLNL) hosted a Women in Data Science (WiDS) regional event on March 2. Held at the HPC Innovation Center, 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...

Local Women in Data Science conference showcases Lab research

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

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...

DSI sponsors LLNL hackathon

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

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...

LLNL-led team awarded Best Paper at SC19 for modeling cancer-causing protein interactions

A panel of judges at the International Conference for High Performance Computing, Networking, Storage and Analysis (SC19) on Thursday awarded a multi-institutional team led by Lawrence Livermore National Laboratory computer scientists with the conference’s Best Paper award. The paper, entitled “Massively Parallel Infrastructure for Adaptive Multiscale Simulations: Modeling RAS Initiation...

Department of Energy researchers share data management strategies at first-ever “Data Day”

It’s become something of a mantra of the digital age: Data is the new currency. Especially in science, where it’s hard to find a single project that doesn’t involve generating or consuming massive amounts of data. In light of the growing awareness of the critical importance of data management across the Department of Energy complex, more than 100 researchers from DOE national laboratories...

Big data illuminates the physical sciences

Livermore teams are applying innovative data analysis and interpretation techniques to advance fundamental science research. This article describes projects in astrophysics and materials science. Read more at Science & Technology Review.