layout University Affiliated Research Center - UC Santa Cruz - NASA Ames Research Center
Research

Information Technology and Computer Sciences

  • Developing advanced information, computer science, and computing technologies in support of NASA’s space exploration and aerospace missions.
  • Applying intelligent agent technologies to emerging missions.
Mission Control Technologies
  • Design and create software out of fine-grained inter-operable components rather than monolithic applications, using ethnographic techniques and collaborative user research with flight controllers to create and validate the design.
  • Deliver that software product to Mission Control, to replace existing applications.
Autonomy and Diagnostics
  • Provide leadership in the broad area of Integrated System Health Management with particular emphasis on model-based diagnosis, prognosis, and recovery methods for systems exhibiting quantitative, dynamic and stochastic behavior.
Planetary Data System (PDS) User Research and Prototyping
  • Conduct user research and use the data to prototype tools for NASA’s Planetary Data System, facilitate user interactions with those tools, and further develop the tools that meet user needs.
Strategic and Tactical Activity Planning Tool Development for Missions
  • Develop or contribute to development of tools that support streamlining of space station operations.   
  • Focus primarily on tools that validate, compare or render the operational plans being generated by Ensemble, Europa or JSC tools, and prototype tools for capturing and managing planning constraints and other inputs to the planning process.
Time Series Analysis and Prognostics for Complex Engineering Systems
  • Performance of time series analysis, anomaly detection, diagnosis, and prediction on data that represents the operation of complex engineering systems primarily as part of the Aeronautics Research Mission Directorate (ARMD) System-wide Safety Assurance Technologies (SSAT) project.
Semantics-based Information Management
  • Develop new integration traceability functionality for the Dig-IT data integration middleware.
Learning in Complex Systems
  • Support research programs under NASA’s Exploration Systems and Aeronautics Mission Directorates.
  • Develop ways to verify and validate software for complex systems.
Automated Design Using Evolvable Hardware
  • Develop and apply advanced search and optimization algorithms and representations for automated computer design in support of NASA’s Space Exploration and Aeronautics goals.
NASA Information Architecture
  • Provide guidance and traceability for developing Agency Information Architectures
  • Provide a basis for uniform representation of information and for active use in the development of software, communication, documentation, and vehicle systems
  • Establish of ongoing working relationships with the stakeholders
  • Provide a definition of the current state of information, information technology systems, and information usage across the 21CGSP and in the supporting KSC organizations
  • Provide a Reference Information Architecture which describes the approach to developing and implementing information architecture across KSC, including uniform representation of information, principles of distributed data lifecycle management, and information usage in software applications, data exchange and communication, documentation, and vehicle and facility systems
  • Provide a Target Information Architecture, which provides specification for data, and information used throughout the 21CGSP in a manner consistent with the KSC Reference Information Architecture
  • Record a set of a requirements identified by Agency stakeholders, including derived and decomposed requirements, and gather relevant supporting material such as Use Cases
  • Support model centric engineering effort, OCT portfolio management effort and other similar efforts.
  • Develop applied Information Architectures for the Kennedy Space Center and the 21st Century Ground Systems Program.
Sparse Machine Learning Methods for Understanding Large Collections of Documents
  • Demonstrate that sparse machine learning can be used as a powerful tool to obtain models of high-dimensional data with high degree of interpretability, at a low computational cost.
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