• Herman “Gene” Ray

    Herman Ray, Ph.D.

    Position: Director, Center for Statistics and Analytical Research
    Phone:
    (470) 578-2829
    Email: hray8@kennesaw.edu
    Location: TP 2400
    Website

  • Jessica Rudd

    Name: Jessica Rudd

    Bachelor’s Degree: Anthropology, Emory University

    Master’s Degree: Public Health, Emory University

    Work History:

    • Leidos, Contracted to CDC Division of Viral Diseases, Analysis and Data Management Activity Team, 2010-2017
    • Rwanda Zambia HIV Research Group, Research Project Coordinator, 2007-2010

    Poster Presentations:

    • Predicting Systolic and Diastolic Blood Pressure from Time Variant Parametric Models with Longitudinal Data. Presented poster at R Day (Nov. 2016).
    • Utilizing SAS(R) to Estimate Rates of Disease from Nationally Representative Databases. Scholarship winning poster for the SAS Global Forum (April 2017).
    • A Comparison of Decision Tree with Logistic Regression Model for Prediction of Worst Non-Financial Payment Status in Commercial Credit. First place poster at SAS Day (April 2017).
    • Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification. SAS Analytics 2017 Poster Winner (September 2017)
    • Nonparametric Estimation of Time-Variant Quantiles and Statistical Models. Conference on Statistical Practice (February 2018)

    Session Presentations:

    • Evaluation of Nonparametric Smoothing Estimation Methods Using Time-Variant Longitudinal Data. Presented at Joint Statistical Meeting (August 2017).
    • Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification. Presented at Conference on Statistical Practice (February 2018)
    • Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification. Presented at SAS Global Forum 2018 (April 2018)

    Peer Review Publications:

    • Rudd J. (in press). Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification. Model Assisted Statistics and Applications.
    • Rudd, M. P. H., GStat, J. M., & Priestley, J. L. (2017). A Comparison of Decision Tree with Logistic Regression Model for Prediction of Worst Non-Financial Payment Status in Commercial Credit.
    • O’Hagan, J. J., Carias, C., Rudd, J. M., Pham, H. T., Haber, Y., Pesik, N., ... & Swerdlow, D. L. (2016). Estimation of severe Middle East respiratory syndrome cases in the Middle East, 2012–2016. Emerging infectious diseases, 22(10), 1797.
    • Assiri, A., Abedi, G. R., Saeed, A. A. B., Abdalla, M. A., al-Masry, M., Choudhry, A. J., ... & Rudd, J. (2016). Multifacility outbreak of Middle East respiratory syndrome in Taif, Saudi Arabia. Emerging infectious diseases, 22(1), 32.
    • Schneider, E., Chommanard, C., Rudd, J., Whitaker, B., Lowe, L., & Gerber, S. I. (2015). Evaluation of patients under investigation for MERS-CoV infection, United States, January 2013–October 2014. Emerging infectious diseases, 21(7), 1220.
    • Rha, B., Rudd, J., Feikin, D., Watson, J., Curns, A. T., Swerdlow, D. L., ... & Gerber, S. I. (2015). Update on the epidemiology of Middle East respiratory syndrome coronavirus (MERS-CoV) infection, and guidance for the public, clinicians, and public health authorities-January 2015. MMWR. Morbidity and mortality weekly report, 64(3), 61-62.
    • Al-Abdallat, M. M., Payne, D. C., Alqasrawi, S., Rha, B., Tohme, R. A., Abedi, G. R., ... & Haddadin, A. (2014). Hospital-associated outbreak of Middle East respiratory syndrome coronavirus: a serologic, epidemiologic, and clinical description. Clinical Infectious Diseases, 59(9), 1225-1233.
    • Payne, D. C., Iblan, I., Alqasrawi, S., Al Nsour, M., Rha, B., Tohme, R. A., ... & Jarour, N. (2014). Stillbirth during infection with Middle East respiratory syndrome coronavirus. The Journal of infectious diseases, 209(12), 1870-1872.
    • Cortese, M. M., Immergluck, L. C., Held, M., Jain, S., Chan, T., Grizas, A. P., ... & Gautam, R. (2013). Effectiveness of monovalent and pentavalent rotavirus vaccine. Pediatrics, peds-2012.

    Professional Objective: To work in data science building analytics pipelines for extraction of meaning from large-scale data

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