Kody specializes in computational approaches to inverse problems, uncertainty quantification, and sequential data assimilation. His interest spans methodology, such as function-space sampling and filtering algorithms, and also applications, such as numerical weather prediction, ocean prediction, climate prediction, and subsurface reconstruction.  His interest in data assimilation relates particularly to the accuracy and stability of filters for high-dimensional systems and the relationship to both nonlinear filtering as a sequential Bayesian inverse problem, on the one hand, and deterministic feedback control, on the other hand.  Examples of systems which are currently of particular interest are fluid dynamical systems as related to numerical weather prediction and oceanography and also porous media systems as related to subsurface reconstruction and oil recovery.  He is generally interested in both sequential and static quantification of uncertainty in high-dimensional systems from both the probabilistic perspective as well as the classical deterministic perspective.  Other such systems may arise, for example, as the discretization of another physical system governed by a PDE or an SDE (from a path-space view) or from other applications in social, biological, or economic sciences.

See Dimension-independent MCMC Sampling, and Data Assimilation and Filtering webpages.