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CRD Researchers Using Deep Learning to Help VA Identify Suicide Risk in Veterans
Researchers in Berkeley Lab's Computational Research Division are applying deep learning methods and data analytics to electronic health record data to help the Veterans Administration address a host of medical and psychological challenges affecting many of the nation’s 700,000 military veterans. Read More »
UniviStor: Next-generation Data Storage for Heterogeneous HPC
The Proactive Data Containers project team at Berkeley Lab is developing object-oriented I/O services designed to simplify data movement, data management, and data reading services on next-generation HPC architectures. Read More »
IDAES Process Systems Engineering Software Now Open Source
IDAES has released the first open-source version of its next-generation computational framework and model library, created to optimize the design of process systems engineering solutions used to model advanced energy systems. Read More »
CRD’s Ice Sheet Modeling Tool Probes Antarctic Vulnerabilities
The BISICLES ice sheet model uses high performance computing resources at the National Energy Research Scientific Computing Center (NERSC) to systematically examine where the Antarctic Ice Sheet is vulnerable and the resulting potential for large contributions to sea level rise. Read More »
New Computational Biosciences Group Aims to Build on Lab Strengths, Break Down Barriers
A new group in Berkeley Lab’s Computational Research Division (CRD) aims to tap into the lab's expertise in both computing and biosciences, crossing organizational lines to create an integrated team to develop new tools for addressing a range of scientific problems. Read More »
CRD Postdoc Posters Honored at SIAM-CSE Meeting
Two postdoctoral researchers in the Computational Research Division’s Center for Computational Sciences and Engineering won “best poster” during the recent SIAM-CSE meeting. Read More »
Mobiliti: A Game Changer for Analyzing Traffic Congestion
Berkeley Lab researchers have developed a software tool that uses supercomputing resources at the National Energy Research Scientific Computing Center to accurately simulate traffic flow throughout the San Francisco Bay Area road networks and provide estimates of the associated congestion, energy usage, and productivity loss. Read More »
NWB:N Consortium Rolls Out v2.0 of Software Ecosystem for Neuroscientists
The Neurodata Without Borders: Neurophysiology (NWB:N) project, led by Berkeley Lab, has achieved yet another milestone with the full release of NWB:N 2.0 in January. Read More »
SRP Workshop Matches Faculty-and-Student Teams with Berkeley Lab Research Collaborators
Nearly 30 faculty members from schools all over the U.S. attended the latest Sustainable Research Pathways matching workshop, held December 4, 2018 in Berkeley Lab’s Shyh Wang Hall. Read More »
Novel X-ray Imaging Technique Provides Nanoscale Insights into Behavior of Biological Molecules
CAMERA researchers have spent the past several years developing a new data analysis approach for fluctuation X-ray scanning that helps avoid motion blur and yields better, more detailed three-dimensional models. Read More »
CRD Scientists Bell, Colella Receive Lab Lifetime Achievement Awards
John Bell and Phil Colella of Berkeley Lab's Computational Research Division (CRD) each received the 2018 Berkeley Lab Prize – Lifetime Achievement Award at a special ceremony on Nov. 30. The Berkeley Lab Prize is presented annually to honor career-spanning exceptional achievements. Read More »
Berkeley Lab Researchers to Build Standards for Neurophysiology Data
Researchers from Berkeley Lab and the Allen Institute for Brain Science will receive $2 million from the National Institutes of Health to develop a next-generation data format and software ecosystem for the Neurodata Without Borders: Neurophysiology (NWB:N) project. Read More »
SENSEI Showcased at SC18
CRD scientists at SC18 are showcasing SENSEI, a lightweight software infrastructure that enables simulations to make use of a wide array of popular in situ analysis and visualization packages. Read More »
CRD Researchers Part of Team Honored with Klaus Halbach Award
CRD staff were among the Berkeley Lab computer scientists, engineers and mathematicians recently honored with the 2018 Klaus Halbach Award for innovative instrumentation at the Advanced Light Source. Read More »
CRD Hosts Emerging Women Leaders
Researchers Ijeoma Ezika of Nigeria and Edith Mugehu of Zimbabwe spent two weeks studying biotechnology and data analytics at Berkeley Lab as part of the U.S. Department of State's TechWomen Emerging Leaders program. Read More »
HP-CONCORD Paves the Way for Scalable Machine Learning in HPC
A team of Berkeley Lab researchers has demonstrated how a new parallel algorithm called HP-CONCORD can help address some of the most challenging problems in data-driven science. Read More »
AMReX Co-Design Center Helps Five ECP Projects Hit Performance Goals
The AMReX Co-Design Center makes available a state-of-the-art AMR infrastructure with the functionality that five ECP application projects and other AMR applications use to be able to effectively take advantage of current and future architectures. Read More »
At Biden Summit, CRD's Ushizima Discusses Using Machine Learning to Improve Cancer Detection
Dani Ushizima, a staff scientist in the Computational Research Division who has adapted algorithms used in materials research to scan for cervical cancer, described her research in a panel discussion at the Sept. 21 East Bay Biden Cancer Community Summit. Read More »
Berkeley Lab to Push Quantum Information Frontiers With New Programs in Computing, Physics, Materials, and Chemistry
A series of DOE Office of Science awards, announced today, will enable Berkeley Lab to accelerate the development of quantum computing. Berkeley Lab Computing Sciences staff will play leading roles in three of the awards. Read More »
Berkeley Lab to Build Advanced Quantum Computing Testbed
The U.S. Department of Energy announced that Berkeley Lab will receive $30 million over five years to build and operate an Advanced Quantum Testbed (AQT). Researchers will use the testbed to explore superconducting quantum processors and evaluate how these emerging quantum devices can be utilized to advance scientific research. Read More »
Berkeley Lab Researchers Co-Author IARIA’s Best Paper
A paper co-authored by Berkeley Lab’s Esmond Ng and Chao Yang won best paper at this year’s International Academy, Research, and Industry Association’s (IARIA’s) Computation Tools 2018 conference in Barcelona, Spain. The paper, Deep Learning: A Tool for Computational Nuclear Physics, describes how feed-forward artificial neural networks can be used to predict the properties of atomic nuclei. Read More »
Berkeley Lab, BIDS Take on Big Data
The BIDS ecosystem comprises an impressive network of Fellows, including some who are Berkeley Lab scientists. This month, several Berkeley Lab-BIDS Fellows are organizing two of events to share their data-science expertise: Machine Learning for Science (ML4Sci) Workshop and the California Water Data Hackathon. Read More »
Exascale Computing Project Spotlights ExaGraph and STRUMPACK/SuperLU Collaboration
Results from a collaboration between the Exascale Computing Project's Sparse Solvers Software Technology project and ExaGraph Co-Design Center showed that the parallel AWPM (approximate-weight perfect matching) code can run up to 2,500x faster than the sequential algorithm on 256 nodes of NERSC's Cori supercomputer. Read More »
High School Summer Student Sanjana Shah Is 'Posit-ive' About Coding
Sanjana Shah, a high school student from the Peninsula discovered her passion for programming when she attended a coding workshop for youth sponsored by a local tech company. She is also the first (and so far, the only) high school student CRD's David Donofrio has ever hired through the Computing Sciences Summer Student program. Read More »
Berkeley Lab Researchers Showcase Deep Learning for High Energy Physics at CHEP
In a plenary talk given at the recent CHEP conference, NERSC and CRD researchers presented findings from a study in which they demonstrated how generative adversarial networks can speed simulations in high energy physics studies. Read More »
