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    Luddy School of
    Informatics, Computing, and Engineering

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    • Bryan Stephens

    Bryan Stephens, Ph.D.

    Clinical Associate Professor, Library and Information Science

    Email:
    bs22@iu.edu
    IT 589

    Google Scholar profile

    Education

    • Post-Doctoral Fellowship, Duke University Fuqua School of Business
    • Ph.D. and M.P.P., University of Texas at Austin
    • B.A., Indiana University-Bloomington

    Biography

    Bryan Stephens is a Clinical Associate Professor in the Department of Library and Information Science. He brings an interdisciplinary background in sociology, data science, and information studies, with teaching and research that bridge technical skill-building and community impact. He enjoys working with students and organizational partners to advance data-driven projects toward practical, ethical, and community-centered outcomes.

    Professor Stephens teaches in the Data Science program, including courses on Statistical Learning (DSCI-D 415, was INFO-I 415), Deep Learning Neural Networks (DSCI-D 418, was INFO-I 418), Web Mining (DSCI-D 428, was INFO-I 428), and Informatics Capstone (INFO-I 494). His classes emphasize hands-on learning with real-world data and interactive projects. His teaching philosophy centers on lowering barriers to advanced analytics—helping students not only master technical methods but also apply them in meaningful, socially responsive ways.

    Dr. Stephens’s research and applied projects examine how collaboration and information technologies shape innovation, health, and digital equity. He has led student-centered, community-engaged initiatives in the U.S. and abroad, including work in digital inclusion, community data storytelling, and program evaluation for mission-driven organizations. His work has been supported by the NIH and published in Information Systems Research, JASIST, and the Journal of Computer-Mediated Communication.


    Specialty Areas

    • Deep learning and neural networks
    • Statistical and machine learning
    • Community-engaged data science
    • Digital inclusion and equity
    • Data visualization and storytelling
    • Program evaluation and applied analytics
    • Collaboration, innovation, and team science

    Courses Taught

    • DSCI-D 415 Statistical Learning (was INFO-I 415)
    • DSCI-D 418 Deep Learning Neural Networks (was INFO-I 418)
    • DSCI-D 428 Web Mining (was INFO-I 428)
    • INFO-I 494 Informatics Capstone
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    535 W. Michigan Street
    IT 400
    Indianapolis, IN
    317-278-4636

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