About Kody J. H. Law Kody J. H. Law Senior Research Scientist, Stochastic Numerics Research Group uncertainty quantification data assimilation probability theory Kody J. H. Law worked as a Senior Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Research Interests 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 Projects Related Projects 2015 Multilevel ensemble Kalman filtering Thu, Jan 1 - Mon, Jun 1 2015 Kalman filter Filtering is a method for sequentially estimating the state of an evolving dynamical system in settings where only partial and possibly inaccurate measurements of the history of the state are available. 2013 Data Assimilation and Filtering Sat, Jun 1 2013 - Sun, Jun 1 2014 Data assimilation, or filtering, refers to the problem of combining noisy observations of a (typically physical) system together with a model for that system in order to infer the state and/or parameters online as data is received. In the probabilistic context of a hidden-Markov model, this leads to a recursion of Bayesian updates. The objective of the filtering problem is then to obtain the posterior distribution of the unknown as a function of the history of observations. Dimension-Independent MCMC Sampling Algorithms Sat, Jun 1 2013 - Sun, Jun 1 2014 Inspired by the recent development of pCN and other function-space MCMC samplers, and also the recent independent development of Riemann manifold methods and stochastic Newton methods, we propose a class of algorithms which combine the benefits of both, yielding various dimension-independent, likelihood-informed (DILI) sampling algorithms. These algorithms are very effective at obtaining minimally-correlated samples from very high-dimensional distributions.
Multilevel ensemble Kalman filtering Thu, Jan 1 - Mon, Jun 1 2015 Kalman filter Filtering is a method for sequentially estimating the state of an evolving dynamical system in settings where only partial and possibly inaccurate measurements of the history of the state are available.
Data Assimilation and Filtering Sat, Jun 1 2013 - Sun, Jun 1 2014 Data assimilation, or filtering, refers to the problem of combining noisy observations of a (typically physical) system together with a model for that system in order to infer the state and/or parameters online as data is received. In the probabilistic context of a hidden-Markov model, this leads to a recursion of Bayesian updates. The objective of the filtering problem is then to obtain the posterior distribution of the unknown as a function of the history of observations.
Dimension-Independent MCMC Sampling Algorithms Sat, Jun 1 2013 - Sun, Jun 1 2014 Inspired by the recent development of pCN and other function-space MCMC samplers, and also the recent independent development of Riemann manifold methods and stochastic Newton methods, we propose a class of algorithms which combine the benefits of both, yielding various dimension-independent, likelihood-informed (DILI) sampling algorithms. These algorithms are very effective at obtaining minimally-correlated samples from very high-dimensional distributions.
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