Postdoc UQ Group, Department of Aeronautics and Astronautics Massachusetts Institute of Technology
office: 37-431 email:
rmorriso AT mit.edu
PhD in Computational Science, Engineering, and Mathematics, UT Austin, 2016
Designing data-driven models that respect physical constraints/information
Probabilistic graphical models and sparsity of Markov random fields
Mathematical representations of model inadequacy
High-dimensional Bayesian modeling and model inversion
Calibration, validation, and uncertainty quantification for predictive
Publications & Reports
R. E. Morrison, R. D. Moser, T. A. Oliver. Representing model inadequacy: A stochastic operator approach. To appear in SIAM Journal of Uncertainty Quantification.Arxiv.
R. E. Morrison, R. Baptista, Y. Marzouk. Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting. NIPS 2017.
R. E. Morrison. On the representation of model inadequacy: A stochastic operator approach. Dissertation in Computational Science, Engineering, and Mathematics. ICES,
UT Austin, January 2016.
R. E. Morrison, C. Bryant, G. Terejanu, K. Miki, S. Prudhomme. Optimal
data split methodology for model validation. In proceedings of and presented
at the World Congress on Engineering and Computer Science. San Francisco, CA,
R. E. Morrison, A. S. Landsberg, E. J. Friedman. Combinatorial Games
with a Pass: A dynamical systems approach. Chaos
21, 043108; 2011. Arxiv.