Mathematical Sciences Institute, Australian National University

Dr Stals has a strong background in the implementation of scalable algorithms on high performance computers. In the quest to simulate successively larger and more realistic models, it has become evident that such goals can not be fully achieved without addressing the design of scalable algorithms; and to realise scalable algorithms attention must be paid to both mathematical and computing ideas. Dr Stals has a unique set of skills that allow her to build on the interface between these two disciplines.

Dr Stals has implemented a parallel multigrid method program based on adaptive finite elements. The code developed as part of this research has formed the basis of projects designed to study applications such as: plasma ion implantation, flow through heterogeneous material (in combination with the DOUG code), radiation transport equations and thin plate splines. She has also studied cache aware multigrid methods and is currently looking at resilient iterative solvers.

In 2014, Dr Stals was awarded a College Commendation For Outstanding Contribution to Student Learning, and nominated for a university citation; and was awarded the status as a Senior Fellow of The Higher Education Academy in 2015.

Dr Linda Stals will be lecturing at AMSI Winter School 2017, delivering a course on “Large Scale Matrix Problems”.

Dr Linda Stals


1. Can you tell us about your work? What drives your interest in this field?I view my work as sitting on the interface between computing and mathematics. What interests me is how the two disciplines influence and interact with one another. We are taught in first year that Gaussian Elimination is the method for solving systems of equations. However, when implemented on a computer the method does not work well on larger problems, it is too slow and requires too much memory. Hence it was necessary to go back to the mathematics and view the solution techniques in different ways and develop new mathematical tools that are of more practical use.

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

There are a number. One that I am currently exploring is resilient algorithms. As the number of processors in a supercomputer increases, so does the probability that a fault will occur. Traditional check-pointing techniques may require too much energy and memory. An alternative approach is to develop fault recovery procedures with in the numerical algorithms.

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

The area of scientific computing has been termed as ‘the third way to do science’. The traditional ways are theory and experimentation; the third way is computer simulation. It is relatively cheap and a lot safer to smash a car on a computer.

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

The implementation of a technique that has only been done by a handful of groups worldwide (and usually by teams of people), and which has been of practical use in several different projects.

5. Why did you become a mathematician/statistician? 

I studied mathematics because I am passionate about science and mathematics is the language of science. During my career, I have been fortunate to be able to work with people from many different areas such as geologists, astronomers, physics, and economists. This has given me the opportunity to get to know more about these disciplines from those who know their area.

6. Do you have any advice for future researchers?

Pick a topic that you are strongly interested in, because you will inevitably hit a brick wall and will need to be strongly motivated to get over it.