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Multiscale Fast and Distributed Data and Statistics Summarization

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911NF-18-C-0082
Agency Tracking Number: A2-7311
Amount: $500,000.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: A17A-T010
Solicitation Number: 17.A
Solicitation Year: 2017
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-09-28
Award End Date (Contract End Date): 2019-09-28
Small Business Information
15400 Calhoun Drive Suite 190
Rockville, MD 20855
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Chris Kurcz
 Senior Research Scientist
 (301) 294-4620
Business Contact
 Mark James
Phone: (301) 294-5200
Research Institution
 Johns Hopkins University
 Mauro Maggioni Mauro Maggioni
3400 North Charles Street 600N Wyman Park Building, Office of the Dean
Baltimore, MD 21218
United States

 (410) 516-6525
 Nonprofit College or University

Over the last decade the amount of data available from the internet, sensors and other sources has grown dramatically providing the opportunity to gain novel insights in many fields. Uncovering non-linear low dimensional structure in high-dimensional data (i.e., manifold learning), a key to summarization, remains a challenging problem which ultimately inhibits knowledge discovery. Intelligent Automation Inc, in collaboration with Johns Hopkins University proposes the developed and application of scalable and robust multiresolution summarization algorithms to several relevant problems. The methods proposed are based on Geometric Multi-Resolution Analysis and Diffusion Geometries which enjoy provable accuracy and scaling guarantees. These approaches have many favorable characteristics including linear scaling with the number of data points, multi-resolution representation of the data, robust to noise, provable error estimates, amenable to fast algorithms, and suitability for visualization and subsequent analysis. We will develop parallel and distributed implementations to support execution of summarization method on large amounts of high dimensional in data intensive computing environments. To demonstrate the benefits of these methods will we develop a visualization capability that enable interactive knowledge discovery and apply them to problems in text and hyperspectral image analysis.

* Information listed above is at the time of submission. *

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