Machine Learning & Analytics Group

The Machine Learning & Analytics Group engages in research, development, and deployment of scalable, high performance, and data-intensive machine learning, analytics, and visualization technologies in collaboration with other researchers from diverse scientific disciplines. These methods and their application help to uncover new knowledge in scientific data from simulations, experiments and observations.

We accomplish this mission by

Focusing RD&D efforts at all stages of the data understanding pipeline.
Collaborating closely with science stakeholders to maximize science impact.
Tightly integrating and coordinating interaction between research, development, and deployment activities.

Programming Model Bake-off Helps Inform Data Analysis Directions in Post-Moore’s Era (More information)

Accelerating Optimization of a Probabilistic Graphical Model through Data-Parallel Primitives (More information)

Nested Tracking Graphs Show Relationship of Features in Simulations Over Time (More information)

Accelerating Graph Analytics through Data-Parallel Primitives (More information)

MSMcam: Materials Segmentation & Metrics (More information)

BASTet: Berkeley Analysis and Storage Toolkit

NWB:N -- Beta of Neurodata Without Borders 2.0 Format and Software Released

BrainFormat: A Data Standardization Framework for Neuroscience Data

Petascale Study of Ion Foreshock Dynamics (More information)

Visible-Wavelength Polarized Light Emission with Small-Diameter InN Nanowires (More information)

OpenMSI: A Science Gateway to Sort Through Bio-Imaging's Big Datasets

Flying Through the Known Universe in 3D (More information)

CO<sub>2</sub> Sequestration and Storage: From Raw Micro-CT to Quality Measurements — CO₂ Sequestration and Storage: From Raw Micro-CT to Quality Measurements (More information)

Parallel Stream Surface Computation for Large Data Sets (More information)

Environmental Management: Hanford Site Nuclear Waste Clean-up (More information)

High-performance Computing for Computational Finance (More information)

Linking PointCloudXplore and Matlab: Making Advanced Analysis Easily Accessible (More information)

Query-Driven Analysis of Large Scale Time-dependent Data (More information)

High-throughput Characterization of Porous Materials (More information)

Performance Optimization and Auto-tuning (More information)

Parallel I/O, Analysis, and Visualization of a Trillion-Particle Simulation (More information)

Pattern Detection in Climate Data (More information)

Topological Cacti: Combining Structural and Quantitative Information (More information)

MetroMaps: Map-based Representations for Analyzing Optimization Solution Spaces (More information)

Efficient Parallel Extraction of Crack-free Isosurfaces from Adaptive Mesh Refinement Data (More information)

Streamline Integration using MPI-Hybrid Parallelism on a Large, Multi-Core Architecture (More information)

High Performance Visualization: Enabling Extreme Scale Scientific Insight (More information)

News

January 19, 2021

We are now the Machine Learning & Analytics Group, a name change that reflects our increasing focus on AI/ML technologies and their application to DOE mission science problems.

April 24, 2020

Aditi Krishnapriyan joins the Data Analytics and Visualization Group.

June 4, 2019

NWB:N Project Hosts User/Developer Days. (Read more at the CS web site)

Feburary 4, 2019

NWB:N Consortium Rolls Out v2.0 of Software Ecosystem for Neuroscientists. (Read more at the CRD web site)

November 20, 2018

Berkeley Lab Researchers to Build Standards for Neurophysiology Data. $2 Million NIH Grant to Fund Neurodata Without Borders Project. (Read more at the CS web site)

November 12, 2018

Loring co-authors ISAV 2018 Honorable Mention Paper.

November 2, 2018

DAV Staff headed to Dallas for SC18. (Read more at the CS web site)

July 9, 2018

"Women @ The Lab" recognizes DAV's Daniela Ushizima. (Read more at the LBL Women at the Lab website)

June 26, 2018

Berkeley researchers use machine learning to search science data (Read more at the CS web site).

Machine Learning & Analytics Group

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