DCU’s Dr Martin Crane is part of a team that hopes to make the most of Brexit from a fintech perspective, but also in analysing data in everyday life.
Could there be some benefits, particularly for researchers, to be found amid the fallout of Brexit?
From a research perspective, one of those hoping to find out is Dr Martin Crane, associate professor at Dublin City University’s (DCU) School of Computing and director of the Advanced Research Computing Centre for Complex Systems Modelling (ARC-SYM), which was created to address complex and dynamic problems in science.
After completing an undergraduate degree in mechanical engineering from Trinity College Dublin (TCD), he went on to do a PhD in the college’s School of Computer Science, followed by a postdoc in Glasgow’s Strathclyde University.
Following a stint in Hitachi in the mid-1990s, Crane moved to DCU’s School of Computing before taking up his current role at ARC-SYM.
What inspired you to become a researcher?
I was always a bit reluctant to embark on an academic career.
However, my early research during my final-year project on flow-induced resonance in the Parsons Building in TCD launched a passion for that subject, and time-series data in particular has driven my research to this day.
Can you tell us about the research you’re currently working on?
I have just finished working with a team of major academic and industry partners on a large proposal for funding in the fintech area in collaboration with the Science Foundation Ireland-funded Adapt research centre based in DCU.
The proposal arose from a shared realisation among the partners that fintech was a potential major growth area in this country with the impact of Brexit as well as major technological advances in the area of banking and finance.
In your opinion, why is your research important?
The work that we are doing in fintech is just part of the wider application of complex systems involving data from real life.
I see the subject of complex systems in general as important because of the growth in data from such sources as:
- sensors (including the internet of things, P2P networks, vehicular social networks, among others)
- financial systems at ever-varying granularities in different asset classes
- multiomics genome data regulated at multiple levels
- internet traffic data from sources as diverse as social networks to cybersecurity
Further, both computational resources and commensurate information requirements continue to increase.
What commercial applications do you foresee for your research?
I see many commercial applications in the area of complex systems. Some examples include:
- The necessity among drugs companies for models of the entire human immune system from a micro to macro level
- A growing need in real time for data and patterns on internet traffic with applications including data centres, power stations and online gaming
- The necessity to understand more about swarms of endangered species and other ecological applications in the context of climate change
- The growing importance of smart cities as complex systems with inhabitants interacting with the environment and each other
What are some of the biggest challenges you face as a researcher in your field?
In large part, a lot of the challenges that I face are due to a lack of confidence in the abilities among many people as a result of their perceived lack of science or maths training.
This lack of confidence makes them resistant to informing themselves on essential or basic technical matter.
Are there any common misconceptions about this area of research? How would you address them?
There is a tendency to confuse complex systems with complicated systems and this gives rise to the resulting – and almost automatic resistance to – things that are seen as difficult to understand in any sense.
Really, the main answer in both cases is education and communication.
As our world is seen as an increasingly interdependent system, with the behaviour of one being dictated and affected by all others, there will be a commensurate need for models to explain this behaviour and predict any implications.
What are some of the areas of research you’d like to see tackled in the years ahead?
I see human brain research as hugely important in the future with the ageing population trend.
In this space, great work is being done as part of the EU-funded Human Brain Project, which carries out interdisciplinary research in neuroscience, advanced simulation and multiscale modelling, and brain-related medicine.