Los Alamos National Labs with logo 2021

Information Sciences

Uncovering actionable knowledge and generating insight into exascale datasets from heterogeneous sources in real time


  • Deputy Group Leader
  • Kim Kaufeld
  • Email

Contact Us  

  • Group Administrator
  • Iris Eguino
  • Email
Conceptual illustration of futuristic data stream processing.

Developing methods and tools for understanding complex interactions and extracting actionable information from massive data streams.

Basic and applied research supporting national security science

Teams and Thrust Areas

We conduct basic and applied research related to systems that make intuitive sense of the world around us.  From the development of novel deep-learning neural architectures for data-driven decision making, to the invention of new algorithms that take advantage of revolutionary hardware such as quantum and neuromorphic circuitry, we seek to change the world by pushing the boundary of what is possible in the here and now. Our core activities fall into six different areas:

Machine Learning

Developing computational methods that “learn by example” through the development of mathematical models, using past observations to make predictions about future data. Our research team is at the forefront of developing new methodologies for data-driven decision making, and applying these techniques to a wide variety of real-world applications.

Novel Computing

Algorithm development for non-traditional computational models and systems.  We have active research programs related to quantum computing, neuromorphic computing, and developing custom ASICs to serve as coprocessors to accelerate critical computations.

Computer Vision

We develop methods for analyzing and extracting information from all types of spatio-temporal data sets. This includes image, video, and signal analysis, as well as the output of physics simulation codes.  Making sense of data sets such as these is a fundamentally important activity to help in their proper analysis and interpretation.

Combinatorics & Optimization

As the world generates increasingly large data sets to analyze, efficient algorithms are crucial to finding answers to the important scientific questions we face.  We develop innovative approaches to difficult computations, and seek new ways to leverage all possible computing technologies in ways that enable us to solve problems never before possible. This includes fundamental algorithms for graph theory and quantum optimization, as well as large-scale control of physical systems.

Data Science at Scale

Extremely large datasets and extremely high-rate data streams are becoming increasingly common due to the operation of Moore's Law as applied to sensors, embedded computing, and traditional high-performance computing. Interactive analysis of these datasets is widely recognized as a new frontier at the interface of information science, mathematics, computer science, and computer engineering. Text searching on the web is an obvious example of a large dataset analysis problem; however, scientific and national security applications require far more sophisticated interactions with data than text searches. These applications represent the ‘data to knowledge’ challenge posed by extreme-scale datasets in, for example, astrophysics, biology, climate modeling, cyber security, earth sciences, energy security, materials science, nuclear and particle physics, smart networks, and situational awareness. In order to contribute effectively to LANL's overall national security mission, we need a strong capability in Data Science at Scale. This capability rests on robust and integrated efforts in data management and infrastructure, visualization and analysis, high-performance computational statistics, machine learning, uncertainty quantification, and information exploitation. The Data Science at Scale capability provides tools capable of making quantifiably accurate predictions for complex problems with the efficient use and collection of data and computing resources.

Space Architectures

The Space Architectures Team spans cutting-edge computer science research, FPGA design, signal processing, modern software engineering, integration of new technologies, and support of hardware-software co-design. Our team bridges the gap between the laboratory's computer science research activities and the custom hardware application needs of NNSA Defense Programs. Working as the bridge between Applied Computer Science and Global Security we develop new technologies for custom hardware systems, particularly for space applications. Through partnerships with mission application teams we facilitate rapid adoption of modern and cutting edge hardware and software practices and technologies to improve the performance, flexibility and reliability of new systems and missions.

Some of the programs we support include:

  • The United States Nuclear Detonation Detection System
  • Prometheus Small Sat architecture
  • The Joint Architecture Standard for Space (LANL/Sandia)
  • 3CEL/Gryphon multi-phenomenology satellite cluster (LANL/Sandia/LLNL/AFRL)
  • Quantum (Key Distribution + Computing/DWave)
  • LDRD Mission Foundations Research "Coherence through Computation Aperture Synthesis"

Capabilities include:

  • Custom hardware design/Mentor Graphics schematic capture
  • Firmware design (VHDL)/Simulink-based firmware capture/System-on-a-chip development
  • Software development (C/C++/Python)
  • RF Signal Processing
  • Accelerated Computation (FPGA, cluster/MPI, GPU)
  • Machine Learning (TensorFlow, Movidius, graph analytics)
Software Downloads

  • Deep Abstaining Classifier (Label Denoising for Deep Learning)
  • Sequedex (DNA Sequence Classification)
  • Simian (Just in Time Compiled Parallel Discrete-Event Simulator)
  • SimX (Python-based engine for Parallel Discrete-Event Simulation)
  • PetaVision (C++ library for designing and deploying large-scale neurally inspired computational models)
  • LUNUS (Software for diffuse X-ray scattering from protein crystals)
  • PPT (Performance Prediction Toolkit)