Meet Stephen Marsland – a professor of scientific computing in the computer science cluster of the School of Engineering and Advanced Technology (SEAT) at Massey University. Stephen is also Te Pūnaha Matatini’s new Theme Leader: Complex Data Analytics. “Data is cool at the moment… but it would be nice to see people using it well and understanding what they can and can’t infer from analysis,” Stephen says. Find out more about Stephen’s research and what he hopes to achieve in his new role in the below Q&A.
Tell us about your research, including projects aligned with Te Pūnaha Matatini
My first area of research is the mathematics of shape analysis. This is primarily concerned with geodesics on the diffeomorphism group, which is a mathematical way of describing how flows of smooth, invertible transformations can deform one shape into another in the shortest possible way. I also study invariants to the actions of the groups that can deform images.
More related to Te Pūnaha Matatini is my work in machine learning, which has two parts at the moment: I’m thinking about manifold learning, where we try to find low-dimensional representations of high-dimensional data, and I’m also thinking about dealing with learning about multiple sources of data where all that you see is the combination of the sources. The first is a popular question, but I’m thinking about it very much from the point of view of differential geometry, and how that can help. I’ve got multiple projects going on there with collaborators in England and China.
The second project is with Marcus Frean, another Te Pūnaha Matatini principal investigator. So for example, you might see images of an object on different backgrounds, and you want to work out that the object and the background are different pieces of information.
I’ve got a very big project called AviaNZ going on that combines the shape analysis and machine learning, which is looking at birdsong recognition, in the hope that we can develop algorithms that will recognise birds from their calls and then infer the number of birds from how they are calling.
Finally, I’m interested in complex systems in their classic sense, both complex networks (which are networks with properties such as scale-freeness, or that are small worlds) and also systems where the interactions between agents cause the emergence of high-level properties. I’ve got a variety of projects with students looking at this, including in health, marriage systems, and soon, the evolution of barter (this last one will be funded by Te Pūnaha Matatini).
What attracted you to the role of Theme leader: Complex Data Analytics?
Data analytics underlies everything that we are trying to do in Te Punaha Matatini, but it isn’t really getting the recognition as a subject in its own right. I’m hoping that by exploring more of the links with the other themes I can make people more aware of how much data analytics there is going on, and what tools are available.
How can complex data analytics benefit New Zealand?
Data is cool at the moment (big data is mentioned everywhere) but it would be nice to see people using it well and understanding what they can and can’t infer from analysis. We collect data everywhere on everything, but lots of it doesn’t actually get used for much. For example, there are thousands of automatic recorders around New Zealand recording birdsong. But unless you have tools to analyse the data, you’ve just got a lot of memory used up storing sound that nobody will ever pay any attention to. Turning data into information isn’t easy, but it has to be done, and done well, to make the collection of the data worthwhile.