What is your name and your position title? What did you study at undergraduate level and when did you graduate?
My name is Ryan Loxton and I am a professor of applied mathematics at Curtin University. I have been at Curtin since 2001 when I started a double degree in engineering and computer science. I later switched to a mathematics degree, graduating with honours at the end of 2006. After finishing my honours, I commenced a PhD in applied mathematics.
What has been your career path to get to where you are now?
Bachelor of Science (Honours) in Mathematics, then PhD in applied mathematics, both at Curtin.
Have you always been in academia, and if so, what positions have you held along the way?
I have worked continuously as an academic since completing my PhD in 2010. At the same time, I have also been involved in regular consulting projects since 2014 with companies in the mining, oil and gas, and manufacturing sectors. So I have been fortunate to get experience in both academia and industry.
What do you teach? What subjects/units do you currently teach?
I currently don’t have any teaching duties, but when I am teaching I will normally teach advanced (third-year or masters level) units in mathematical optimisation. These units are related to my industry consulting work and they are all about using mathematical techniques to optimise complex systems.
What is your favourite topic/field to teach?
I like teaching most areas of applied mathematics. The advanced mathematics units that we teach at Curtin are on topics like numerical analysis, optimisation, and mathematical modelling, which are all related to my research and consulting work.
What is it about your classes that students enjoy most? What do they enjoy least (if anything)?
Students most enjoy seeing real industry case studies. Mathematics is often taught in a very abstract way, and the abstract nature of mathematics is important, but it’s also important to show students how mathematics connects to the real world. I like to take students through the full end-to-end process of formulating a problem, analysing the theoretical properties and mathematical structure of the problem, and then using these properties to develop an algorithm for finding a solution.
Students least enjoy mathematical proofs! Although the theory and proofs are difficult, they are essential to gaining an in-depth understanding that is needed to tackle the really difficult problems we see in practice. Unfortunately, the problems arising in the real world are not “nice” and they are typically beyond the capabilities of existing software platforms. The mathematical theory allows us to cut down the complexity and generate good solutions in a reasonable period of time.
What are your professional interests?
The most rewarding part of my job is working with companies and using my mathematical/quantitative skills to add value to their operations. It’s very satisfying to see an algorithm that I developed being used in the real world to make a complex process more efficient, less costly, and more sustainable.
What are you working on now?
Various applied research projects with companies and government organisations. The biggest project that I am involved with is the Australian Industrial Training Centre for Transforming Maintenance through Data Science. This research centre is sponsored by the federal and state governments along with mining companies Alcoa, BHP, and Roy Hill. The focus of the centre is to develop new data science methods for optimising the way maintenance is conducted on mine sites, and mathematical optimisation is a big part of this. Maintenance is one of the major operating costs for mining companies and it’s also incredibly complex because you are dealing with massive multi-billion dollar assets and thousands of pieces of equipment. How do you schedule the maintenance activities to minimise time, cost, and re-work?
What do you think is the most important current issue in your field?
Raising the level of mathematics taught in high school. As data science impacts more and more fields, our school leavers need more quantitative skills, not less. This is the case for many courses across science and engineering and beyond, not only mathematics/statistics.
Why should people apply to study the degree related to the subjects you teach?
Mathematics is the foundation of data science. A good grounding in mathematics and statistics gives you an excellent platform to become a leader in this field. The key to being able to adapt to the latest trends is having strong fundamentals in mathematics, statistics, programming, and algorithm design. These concepts are core for any quantitative job.
What pieces of advice would you give to someone who has just enrolled in a master’s degree in your field?
Take risks and challenge yourself. I was far too risk-averse when I was younger and this held me back for many years.