Overview
The Intelligent Data Understanding (IDU) Group collaborates with domain scientists in the fields of Integrated System Health Management, Aeronautics, Space Exploration, Earth Sciences and Space Sciences to answer pressing scientific questions in the field of machine learning, knowledge discovery, and related areas. We also do fundamental research to create tools and methods to aid in the assimilation and understanding of scientific and engineering data to best advance NASA's missions.
Technical Overviews
Orca: A Program for Mining Distance-Based Outliers
The Inductive Monitoring System (IMS) (PDF)
Recurring Anomaly Detection System (PDF)
Using sequenceMiner to Discover Anomalous Flights (PDF)
Intelligent Data Understanding for Earth and Space Science (PDF)
Probability Collectives for Science and Engineering (PDF)
Intelligent Data Understanding for Integrated Systems Health Management (PDF)
Project List
Project Lead: Dawn McIntosh
Numerous disciplines, including aeronautics, physical sciences, and space exploration, have benefited from recent advances in data and text mining, machine learning, and statistics.The objective of the annual Data Mining in Aeronautics, Science, and Exploration Systems conference is to facilitate cross-disciplinary awareness and in-depth discussions between members of the aeronautics, science, industry, academia, and space exploration communities.
+ Visit Annual Data Mining Conference
Project Lead: Dawn McIntosh
The Recurring Anomaly Detection System (ReADS) team is developing a family of novel methods to mine text documents and identify recurring anomalies across reports. ReADS analyzes text reports, such as aviation reports and problem or maintenance records, uses text clustering algorithms to group loosely related reports and documents, and identifies interconnected reports. The tool provides a visualization of the clusters and recurring anomalies, and has been integrated into a secure web-based search platform to allow users to perform their own text mining.
+ Visit Detecting Recurring Anomalies in Text Reports
Project Lead: David L Iverson
The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring.
Project Lead: Ashok N. Srivastava, Ph.D.
Project Lead: Ashok N. Srivastava, Ph.D.
Data mining researchers at NASA Ames Research Center's IDU Group are working with rocket propulsion experts at other NASA centers and at Pratt & Whitney Rocketdyne to apply data mining algorithms to historical data from the Space Shuttle Main Engine (SSME) for real time prognostics and diagnostics.
+ Visit Liquid Propulsion System Health Management
Project Lead: Ashok N. Srivastava, Ph.D.
A method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density estimate.
Project Lead: Ashok N. Srivastava, Ph.D.
Use self-organizing map (SOM) neural networks to identify year-to-year variability of terrestrial ecosystems associated with fluctuation in global circulation and climate.
Project Lead: Mark Schwabacher, Ph.D.
Orca mines distance-based outliers. That is, Orca uses the distance from a given example to its nearest neighbors to determine its unusualness.
Project Lead: David Wolpert, Ph.D.
Unify game theory and statistical physics via the mathematics of information theory, with applications in distributed control for multi-agent systems.
+ Visit Probability Collectives
Project Lead: Ashok N. Srivastava, Ph.D.
An approach to model the behavior of discrete switch sequences in an aircraft using flight data, in order to discover atypical behavior of possible operational significance.
Project Lead: Ashok N. Srivastava, Ph.D.
Methods of predicting missing remote sensing spectra using other relevant data: Virtual Sensors uses models trained on spectrally rich data to "fill in" unmeasured spectral channels in spectrally poor data, thereby extracting more information from spectrally poor data.
+ Visit Virtual Sensors for Earth Science
Project Lead: Ashok N. Srivastava, Ph.D.
+ Visit Virtual Sensors for Space Science
Team
Group Lead
Ashok N. Srivastava, Ph.D.
Group Members
Francesca Barrientos, Ph.D.
Joseph Castle
Santanu Das, Ph.D.
Elizabeth Foughty
Paul Gazis, Ph.D.
Dave Iverson
Rodney Martin, Ph.D.
Bryan Matthews
Dawn McIntosh
Nikunj Oza, Ph.D.
Mark Schwabacher, Ph.D.
John Stutz
Petr Votava
John Wallerius
David Wolpert, Ph.D.
Current Affiliates
Mike Berry, Ph.D
Aditi Chattopadhyay, Ph.D.
Rama Nemani, Ph.D.
Past Affiliates
Ram Akella, Ph.D. - UCSC, UARC
Peter Brende - SIVD - UCSC, UARC
Suratna Budalakoti-RIACS
Robert Delgadillo-FCCD Internship
Vesselin Diev - UCSC, UARC
Gregory Dorais, Ph.D.
Darren Galaviz - FCCD Internship
Michelle Ho - SHARP Internship
Upender Kaul, Ph.D. - NASA
Rebekah Kochavi - QSS
Sakthi Preethi Kumaresan - UCSC, UARC
Alex Lotch - Boston University
Bill Macready, Ph.D. - UARC
Marianne Mosher, Ph.D. - NASA
Manos Pontikakis - UCSC, UARC
Avik Sarkar - Open University, U.K.
Smadar Shiffman, Ph.D. - QSS
David Thompson, Ph.D. - NASA
Len Trejo, Ph.D. - NASA
Eugene Turkov
Richard Watson
Bing Xu - UCSC, UARC
Brett Zane-Ulman - CSC
Yi Zhang, Ph.D. - UCSC, UARC
