• Herman Ray, Ph.D.Dr. Herman “Gene” Ray, Ph.D.

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

  • Lauren Staples

    Name: Lauren Staples

    Bachelor’s Degree: Biosystems Engineering, Clemson University

    Master’s Degree: Statistics, University of New Hampshire

    Work History: Children’s Oncology Group, Clinical Statistician, 2016-2017

    Courses taught: STAT 8940 Applied Analysis Project with Nuesoft. Co-taught with Dr. Ni

    Selected Publications/Presentations: An Optimized Route for Q100’s Bert and Kristin to Visit all Jersey Mike’s Subs in Atlanta for Charity

    Service and Awards: 3rd Place at 4/20/2018 KSU Analytics Day

    Professional Objective: Healthcare Analytics

    LinkedIn

     

    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

    Areas of Research Interest: Data Preprocessing Frameworks, Normalization, Feature Engineering, Bias-Variance Tradeoff

    Google Scholar

    Research Gate

    LinkedIn

     

    Christina Stradwick

    Name: Christina Stradwick

    Bachelor’s Degree: Music Performance and Mathematics, Marshall University

    Master’s Degree: Mathematics with Emphasis in Statistics, Marshall University

    Courses Taught: Prep for College Algebra at Marshall University

    Selected Presentations:

    • Stradwick, C. Exploring the Variance of the Sample Variance. Spring Meeting of the Mathematical Association of America Ohio Section, University of Akron, 2019.
    • Stradwick, C., Vaughn, L., Hanan Khan, A. Data Modeling on Insurance Beneficiary Dataset. College of Science Research Expo 2018, Marshall University, 2018. Poster Presentation.
    • Stradwick, C. Disease modeling on networks. The 13th Annual UNCG Regional Mathematics and Statistics Conference, University of North Carolina at Greensboro, 2017. Poster Presentation.

    Professional Objectives: To work as a researcher in industry or in a laboratory setting. I would like to use my background in mathematics and statistics to develop novel solutions that address limitations in current data science techniques and to apply known data science methods to solve real-world problems.

    LinkedIn

 

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