Data Science in the News

Lawrence Livermore computer scientist heads award-winning computer vision research

The 2021 IEEE Winter Conference on Applications of Computer Vision (WACV 2021) on Wednesday announced that a paper co-authored by LLNL computer scientist Rushil Anirudh received the conference’s Best Paper Honorable Mention award based on its potential impact to the field. The paper, titled "Generative Patch Priors for Practical Compressive Image Recovery,” introduces a new kind of prior—a...

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...

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...

The internship that launched a machine-learning target revolution

Kelli Humbird came to LLNL as a student intern and became a teacher of new data science techniques. In this profile, she describes her experiences and the path that led to her research inertial confinement fusion. Read more at the National Ignition Facility.

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...

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...

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

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...

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.

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...

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...

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...

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.

Successful simulation and visualization coupling proves the power of Sierra

As the first National Nuclear Security Administration (NNSA) production supercomputer backed by GPU- (graphics processing unit) accelerated architecture, Sierra’s acquisition required a fundamental shift in how scientists at Lawrence Livermore National Laboratory (LLNL) program their codes to take advantage of the GPUs. The majority of Sierra’s computational power—95 percent of its 125...

Cindy Gonzales forges a new career in data science

Through LLNL’s Data Science Immersion Program, Gonzales is now among the Lab’s newest data scientists. For two and a half years, she juggled a demanding workload—coordinating Computing’s Scholar Program, interning with data scientists, learning from mentors, supporting LLNL’s Data Science Institute, and attending college part time—while also having her first child. Read more at LLNL Computing...

Collaboration drives data science workshop

Lawrence Livermore National Laboratory’s (LLNL’s) Data Science Institute (DSI) hosted its second annual workshop on July 23–24, 2019. Co-sponsored by the University of California (UC) system, the event drew more than 200 participants to Garré Winery in Livermore. A common theme ran throughout both days: Collaboration is always welcome. Indeed, feedback from last year’s workshop inspired a...

New open-air facility will be testing ground for autonomous drones, vehicles and robots

LLNL’s new OS-150 Robotics Laboratory is an outdoor, 8,000 square-foot enclosure that will serve as a proving ground for the autonomous drones, vehicles and robots of the future. Informally known as the "drone pen," the 50-foot-tall rectangular enclosure, located directly across from one of the Lab’s main engineering buildings, is allowing operators to pilot their drones safely and...

Protecting image classification in artificial intelligence

To address vulnerability concerns in image classification, a new subfield of machine learning has emerged called adversarial machine learning, which focuses on the security of machine learning algorithms. Thomas Hogan, a doctoral student of mathematics at UC Davis, spent his summer investigating this new area of research during the National Science Foundation’s Mathematical Sciences Graduate...

Researchers explore machine learning to automate sorting of microcapsules in real-time

Micro-Encapsulated CO2 Sorbents (MECS) — tiny, reusable capsules full of a sodium carbonate solution that can absorb carbon dioxide from the air — are a promising technology for capturing carbon from the atmosphere. To create the caviar-like objects, scientists run three fluids through a series of microfluidic components to create drops that turn into capsules when exposed to ultraviolet...

Machine learning on a mission

Machine learning uses computers to learn from data and make predictions about the environment. As the world generates more data, interpretation becomes more difficult. A smart machine—one that adapts to new information on the fly—can speed up processing and analysis times and improve its accuracy in identification and prediction tasks. Although commercial and consumer applications of ML are...

CASC research showcased at major data science venues

Researchers from LLNL’s Center for Applied Scientific Computing (CASC) are among the Lab’s employees making waves in the data science community, with many prominent accolades, publications, and acceptances in 2018. Data science encompasses some of the hottest technology topics—machine learning (ML), “big data” analysis, artificial intelligence, computer vision, and more—and the Center’s...