Kathleen Lynn Schmidt

Biography

Research Interests

Uncertainty quantification, Bayesian modeling, mixed-effects modeling, sequential design, sensitivity analysis, splines

Professional Membership

Society for Industrial and Applied Mathematics (SIAM) (SIAM)

American Statistical Association (ASA)

Ph.D., Applied Mathematics, North Carolina State University, 2016
M.S., Natural and Applied Science (Mathematics and Chemistry), Missouri State University, 2009
B.A., Mathematics, Missouri State University, 2007

W. J. Schill, R. A. Austin, K. L. Schmidt, J. L. Brown, N. R. Barton, “Simultaneous inference of the compressibility and inelastic response of tantalum under extreme loading,” Journal of Applied Physics, 130(5), pp. 055901, 2021
A. Muyskens, K. Schmidt, M. Nelms, N. Barton, J. Florando, A. Kupresanin, D. Rivera, “A practical extension of the recursive multi-fidelity model for the emulation of hole closure experiments,” Statistical Analysis and Data Mining: The ASA Data Science Journal,2021.
I. J. Michaud, K. Schmidt, R. C. Smith, J. Mattingly, “A hierarchical Bayesian model for background variation in radiation source localization,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment,1002, pp. 165288, 2021.

J. Bernstein, K. Schmidt, D. Rivera, N. Barton, J. Florando, and A. Kupresanin, “Comparison of material flow strength models using Bayesian cross-validation,” Computation Materials Science, 169(109098), 2019.
K. Schmidt, R. C. Smith, J. Hite, J. Mattingly, Y. Azmy, D. Rajan, and R. Goldhahn, “Optimal positioning of mobile sensors using mutual information,” Statistical Analysis and Data Mining: The ASA Data Science Journal, 12(6), pp. 465-478, 2019.

K. Schmidt and R. Smith, “A parameter subset selection algorithm for mixed-effects models,” InternationalJournal for Uncertainty Quantification, 6(5), doi: 10.1615/Int.J.UncertaintyQuantification.2016016469, 2016.
R. Stefanescu, K. Schmidt, J. Hite, R. Smith, and J. Mattingly, “Hybrid optimization and Bayesian inference techniques for a non-smooth radiation detection problem,” International Journal for Numerical Methods in Engineering, doi: 10.1002/nme.5491, 2016.

K. Schmidt, J. Bernstein, N. Barton, J. Florando, and A. Kupresanin, “Sensitivity analysis of strength models using Bayesian adaptive splines,” AIP Conference Proceedings, 1979(140004), doi: 10.1063/1.5044954, 2018.