Algorithms for Threat Detection (ATD)

 
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    CFDA#

    47.049
     

    Funder Type

    Federal Government

    IT Classification

    B - Readily funds technology as part of an award

    Authority

    National Science Foundation (NSF)

    Summary

    The Algorithms for Threat Detection (ATD) program supports research on new ways to use spatiotemporal datasets to develop quantitative models of human dynamics. The objectives include improved representation of complicated group dynamics and the development of algorithms that can process data in near real-time to accurately identify unusual events and forecast future threats indicated by those events.


    The ATD program will support research projects in two topical areas:

    • Projects that aim to develop novel mathematical and statistical algorithms for analysis of large geospatial datasets. Means to quantify confidence levels are desired, as are insights into new spatiotemporal datasets and valuable means of assembling them. Models may range from those that address activities of individuals to those applicable to small groups or entire nations. These models may leverage mathematical research areas including, but not limited to, point processes, time series, dynamical systems, partial differential equations, and optimal control. Models that depend almost entirely on the spatial and temporal aspects of the data are of greatest interest. General applications of interest include threat detection, predictive analytics, human mobility, and human geography.
    • Projects that develop mathematical theory to guide the use of artificial neural networks (ANN) for computer vision tasks. Subjects of interest include cost functions for training ANNs, non-uniqueness of cost function minima, and dependence of performance on factors like training data quality, task complexity, and network depth. Of particular interest is the theory related to transfer learning - modifying an ANN trained for one set of tasks to do a new task, using only a small amount of training data for the new task. DMS and NGA recognize the needs and opportunities for the mathematical sciences community to develop methodology for reducing threats from a variety of sources. This program seeks ambitious and creative research proposals from individual investigators and collaborative groups in the mathematical sciences community. Research collaborations among mathematical scientists and social, behavioral, and economic scientists on the first topic are encouraged.
     

    History of Funding

    Previous projects funded can be seen at https://www.nsf.gov/awards/award_visualization.jsp?org=NSF&pims_id=503427&ProgRefCode=6877&ProgOrganization=DMS&BooleanElement=true&BooleanRef=true&from=fund.

    Additional Information

    The Division of Mathematical Sciences (DMS) at the National Science Foundation (NSF) has a long history of supporting fundamental mathematical and statistical research relevant to the national interest. DMS has formed a partnership with joint proposal review and proposal management with the National Geospatial-Intelligence Agency (NGA) to develop the next generation of mathematical and statistical algorithms for the inference of information from large geospatial datasets.

    Contacts

    John Greer

    John Greer
    4201 Wilson Boulevard
    Arlington, VA 22230
    (571) 557-2944
     

  • Eligibility Details

    Eligible applicants are:
    • Universities and Colleges
    • Non-profit, non-academic organizations
    • For-profit organizations 
    • State and Local Governments
    • Unaffiliated individuals who have the capability and resources to carry out the project.
    • Foreign organizations
    • Other Federal agencies 

    Deadline Details

    Full proposals are due on March 18, 2020 and February 17, 2021. Proposals are to be submitted by the Third Wednesday in February, annually thereafter.

    Award Details

    Approximately $3,000,000 is available in total funding. An estimated 10 - 20 awards will be granted. Average size award will vary. There is no cost share/match requirement. 

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