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Breadcrumb

  1. Research
  2. Data Science & Computing

Data Science &
Computing

Extracting value from
a torrent of data

Pacific Northwest National Laboratory is addressing computationally focused problems in fundamental science, energy, environment, and national security. Computational scientist Kasia Swirydowicz and her colleagues study how to apply auto learning and reasoning to enhance scientific discovery.

Andrea Starr | Pacific Northwest National Laboratory

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Data are everywhere in today’s world. From the phones in our hands, to the networks that run our homes and communities, to the scientific instrumentation that helps us discover the mysteries of the world—the deluge of data is staggering. This size, velocity, and complexity often make it difficult to gain clarity and extract value. 

Pacific Northwest National Laboratory’s (PNNL's) computing research—from basic to applied—encompasses data and computational engineering, high-performance computing, applied mathematics, and semantic and human language technologies. Our scientists and engineers are developing and applying advanced theories, methods, and models to domains in machine learning, data and computing architectures, systems integration, and software and application development.  

By combining engineering and operations domain expertise and creative integration of advanced hardware and software, we deliver advanced computational solutions that address complex data and analytic challenges. Working in multidisciplinary teams, we connect research to engineering to operations, providing the tools necessary to innovate quickly and field results faster. Our strengths are integrated across the data analytics lifecycle—from data acquisition and management, to analysis, to decision support.

With a strong focus on collaboration, PNNL is a partner in the Exascale Computing Project, a collaborative effort of the U.S. Department of Energy’s (DOE’s) Office of Science and the National Nuclear Security Administration designed to meet DOE’s national security and scientific needs. We are also part of the Physics-Informed Learning Machines for Multiscale and Multiphysics Problems Center—or PhILMs. Through this collaboration, scientists are developing physics-informed learning machines by encoding physics knowledge into deep learning networks.

Our computing research approaches are rooted in a scientific perspective founded on basic and applied research and analysis. We partner with government agencies and industry, performing research and development to deliver solutions to protect the nation’s critical strategic assets and address complex scientific challenges affecting energy, biological sciences, the environment, and national security.

Recent News

MARCH 8, 2023
Staff Accomplishment

PNNL Staff Members Receive DOE Secretary Honor Awards

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Logo of the Secretary's Honor Awards
FEBRUARY 21, 2023
Staff Accomplishment

Ján Drgoňa Receives Best Paper Award

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Headshot of Ján Drgoňa
FEBRUARY 20, 2023
Staff Accomplishment

Jung Lee Joins Frontiers in Computational Neuroscience Editorial Board

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Research Highlights

MARCH 2, 2023
Spotlight

Tackling Hard Governing Equations with Machine Learning

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JANUARY 12, 2023
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Got RedEye?

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JANUARY 4, 2023
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Environmental Science Internships Spark Future Careers

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High school intern Jordan Perkins holds a laptop showing the RemPlex website, standing next to mentor researcher Chris Johnson.
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Facilities & Centers

Environmental Molecular Sciences Laboratory Energy Sciences Center Advanced Wireless Communications Lab Seattle Research Center

Related Divisions

AI and Data Analytics Division
Advanced Computing, Mathematics, and Data

Related Links

What is Physics-informed Machine Learning?What are Advanced Computing Testbeds?What is Graph Analytics?Visiting Faculty Program

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