Kody J.H. Law is a professor of applied mathematics at the School of Mathematics at the University of Manchester, specialising in computational applied mathematics. He received his PhD in mathematics in 2010 from the University of Massachusetts, Amherst, and subsequently held positions as a postdoc at the University of Warwick, and senior mathematician at King Abdullah University of Science and Technology and Oak Ridge National Laboratory.
He has published in the areas of computational applied mathematics, physics, and dynamical systems. His current research interests are focused on the fertile intersection of mathematics and statistics; in particular, inverse uncertainty quantification: data assimilation, filtering, and Bayesian inverse problems.
Kody's current research interest lies in the intersection between mathematics and statistics, enabled by computation, and driven by applications.
Coming from a background in computational applied mathematics and partial differential equations, his data endeavours began with inverse problems and data assimilation, both from a classical perspective as well as a probabilistic Bayesian perspective. In this context, data is used to infer the parameters and state of a model which has been derived on first principles, for example to describe a physical system. Prototypical examples are meteorology and subsurface geophysics, where the objective might be either weather prediction or contaminant source inversion and transport, respectively.
Current focal topics are (i) the design and application of stochastic simulation algorithms for solving these types of problems, based on principles of integrated numerical and statistical analysis, and (ii) the design and application of purely data-driven algorithms for inferring surrogate models (machines) in science and engineering applications.