"The problem is, what you see is that those calibrations predict only the experiments that you just finished, not the ones you are about to do. Those models just aren't rich enough for that kind of problem.
"So there's a lot of interesting things like of trying to produce probabilities over models and being able to use non-parametrics to really build complexity into the models while you are doing it."
On another project he is working on, predicting tectonic plate motion from billions of years ago when mineralisation took place on plate boundaries, Durrant-Whyte said that instead of using a differential equation to model magma and plate tectonic motion he did probabilities over different types of motion parameters.
"That allows you to qualify the uncertainty that's involved, but also to integrate all those different types of data - and any other data that might or might not be relevant - to get the best possible model of what's been going on with the plates," he said.
"We're also exploring much more spatially constrained ideas using things like conditional random fields that might actually explain how different parts of a plate connect together and different things like that," he added.
There are many more ways that data scientists can contribute to scientific discovery, Durrant-Whyte said. He encouraged the community to think differently and look outside the usual applications of their work in finance and marketing to help change our understanding of the planet.
"There is a great future out there for data science, for people in the KDD community, to transform [natural] science."
"I hope the projects [I mentioned] are significantly more compelling and interesting than selling adverts on a mobile phone."
Source: CIO Australia
Sign up for CIO Asia eNewsletters.