Data Science | Science @ GSFC

The Data Science Group offers a unified approach to advance science through the application of advanced analytics including artificial intelligence and machine learning in a high-performance computing environment.

Meet Our People

Science as the Driver for Deep Learning

Data Enhancement & Scaling

Transforming raw satellite data into science-ready resources through innovative processing techniques and computational optimization

Science & Application Advances

Applying state-of-the-art models to earth, planetary, solar, astrophysics, and biological/physical science datasets to glean insights

Deep Learning Activities

Applying the most advanced proprietary and open source models to enormous science datasets using GSFC's High Performance Compute and cloud resources

Scientific Software

Develop custom software applications to advance specific science objectives

Deep learning techniques and modern era hardware resources have enabled better and faster science within our groups.

Learn about some of our featured projects below.

Mapping vegetation height in Alaska with an Earth Observation Foundation Model (EOFM) and commercial spaceborne imagery

We demonstrated the ability of frontier Earth Observing Foundation Models to detect subtle vegetation structure changes in Alaska.

Global Landslide Detection for Rapid response (GLiDEr)

Foundation models and High-resolution commercial imagery enable near-real-time detection of new landslides following extreme events.

Framework for Assessing Changes To Sea-level (FACTS)

Improving the FACTS framework to make a high-performance-compute enabled, science-ready software system to scale up simulations and capture detailed performance benchmarks.

The Discover Supercomputer

The centerpiece of the NCCS is the over 129,000-core "Discover" supercomputing cluster, an assembly of Linux scalable units capable of over 6.8 petaflops, or 6,800 trillion floating-point operations per second. Discover is particularly suited for large, complex, communications-intensive problems employing large matrices and science applications, which benefit from its ecosystem of software ecosystem.

Science Managed Cloud Environment (SMCE)

The Science Managed Cloud Environment (SMCE) is an managed Amazon Web Service (AWS) based infrastructure for NASA funded projects that can leverage cloud computing capabilities.

While the SMCE was started to meet the needs of AIST projects, any NASA project that can leverage AWS public-cloud capabilities can get access to the SMCE.

Explore/ADAPT Science Cloud

Explore combines high-performance computing and virtualization technologies to create an on-site private cloud. This managed virtual machine (VM) environment is specifically designed for large-scale data analytics.

We work collaboratively to help you find solutions to data science and big data problems.

We support NASA initiatives encouraging open science while also preserving your intellectual property. We are a fully staffed team with eleven Innovation Lab Team members. When we are not supporting specific projects we are exploring new technologies and applying new techniques using existing use cases so that we can be ready to help advance projects when applicable.

Contact Us

For general inquiries about the Data Science.

Luli Laulu

Project Support Specialist

301-286-9426 faauliuli.a.laulu@nasa.gov

Mark Carroll

Lead

301-614-6974 mark.carroll@nasa.gov

General inquiries about the scientific programs at NASA's Goddard Space Flight Center may be directed to the Center Public Affairs office at 1.301.286.8955.