Social and Decision Analytics

Kathryn Linehan

  • Research Scientist
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Contact

Bio

Kathryn Linehan is an applied mathematician and Research Scientist in the Social and Decision Analytics Division of the University of Virginia Biocomplexity Institute and Initiative. Her current research interests include numerical linear algebra, low-rank matrix approximation and applications, and natural language processing. She earned a B.A. in Mathematics from Hood College, a M.S. in Applied Mathematics and Scientific Computation from the University of Maryland, College Park (UMD), and a Data Science Certificate from the Georgetown University School of Continuing Studies. Kathryn is currently working towards finishing her Ph.D. from UMD. Prior to working for the Biocomplexity Institute, she was an Associate Professor and the Data Science Coordinator at Montgomery College in the Mathematics, Statistics, and Data Science Department.

CV

    • Numerical linear algebra
    • Low-Rank matrix approximation and applications
    • Natural language processing

  • B.A. in Mathematics, Hood College

    M.S. in Applied Mathematics and Scientific Computation, University of Maryland, College Park (UMD)

    Data Science Certificate, Georgetown University School of Continuing Studies

Leveraging Department of Defense Data To Optimize Individual and Team Performance

Through modern data science techniques, we are uncovering the social characteristics of individual and unit performance that drives Soldiers to successfully meet the challenges of rapid technological change they may need to address in future wars. We are recategorizing these data in a social context to capture Army values and Warrior Ethos, such as courage, honor, loyalty, and empathy.
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National Well-being
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Discovering Emerging Trends for U.S. Federally-funded R&D

Natural language processing and machine learning are powerful techniques for extracting knowledge from the volumes of text-based data available. Collaborating with our agency partner, the National Center for Science and Engineering Statistics (NCSES), we are using administrative data from Federal RePORTER to identify the range of R&D topics funded by federal science and technology agencies, including emerging research areas.
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Official Statistics
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