Oak Ridge National Laboratory

Kody Law is a senior research mathematician in the Computer Science and Mathematics Division at Oak Ridge National Laboratory and joint Associate Professor at the University of Tennessee, Knoxville. He received his PhD in Mathematics from the University of Massachusetts in 2010, and subsequently held positions as a postdoc at the University of Warwick and a research scientist at King Abdullah University of Science and Technology.

He has published in the areas of computational applied mathematics, probability, physics, and dynamical systems. His current research interests are focused on inverse uncertainty quantification: data assimilation, filtering, and Bayesian inverse problems.

Dr Kody Law will be lecturing at AMSI Winter School 2017, delivering a course on Data Assimilation: A Mathematical Introduction”

Dr Kody Law


1. Can you tell us about your work? What drives your interest in this field?

I work on computational applied mathematics and statistics in particular data assimilation. Broadly speaking, this involves improving complex models based on incorporating observed data. This topic sits at the interface of the most recent scientific paradigm of computational simulation and the forthcoming one of data-intensive science. It can be viewed both from a classical deterministic perspective or a probabilistic/statistical one. I am also interested in data-intensive inference problems where there is no complex forward model inside the statistical model.

2. What are the most interesting “big questions” or challenges facing researchers in your area?

The big question now is how will the paradigm of simulation converge with the emerging paradigm of data-intensive science? Data assimilation sits at the intersection. As strictly data-driven methods and algorithms evolve (in the absence of complex forward simulations), largely in the commercial and cyber-security sectors and with the objective of handling massive data coming from online sources, can those methods fundamentally inform or improve upon existing data assimilation methods, or vice versa?

3. What are some key industry applications of your work?

Inversion of subsurface properties is a key element of various industrial endeavors, for example, energy harvesting in the form of geothermal, oil, and gas, or contaminant transport. Another big application is numerical weather prediction.

4. What do you consider your biggest achievement to date?

My Springer book “Data Assimilation: A Mathematical Introduction”. It represents a culmination of years of effort in building expertise in this area, and provides a concise introduction to the subject, which I expect to be a valuable asset to graduate students and other researchers.

5. What do you consider your biggest achievement to date? Why did you become a mathematician/statistician? 

There is no exciting story here. I took calculus for business as an undergraduate, and found it was easy for me and hard for many other students. So, I decided that this is a valuable skill I should nurture and pursue. I went to graduate school, and from there one exciting problem has lead to the next.