Image: Stephan Curiskis
What is the most rewarding aspect of your research?
Learning about state-of-the-art methods, applying them in novel ways to real-world data sets, and thinking of how to make further improvements. Much of my research has involved creating systems incorporating techniques from multiple disciplines. I have found researching, combining and extending different analytical methods to be particularly rewarding, especially when they show good results on challenging data sets.
What are the real-world applications of your research?
The output of my research is aimed primarily at understanding the evolution of discussion topics in social media. This work has a wide range of direct applications, from market research and advertising to psychology and user experience research. For instance, knowing the dynamics of topics that generate viral reach within a social network can help marketers to better frame their messaging, and can provide user experience researchers with insights to improve their social platforms. In addition to these applications, a key part of my research is on developing a modelling framework that can find meaning in user generated digital activity over time, using both structured and unstructured data. This framework has applications to many real-world areas where similar data is available.
What ideas do you (or your team) have for future research?
Our research utilises methods from multiple disciplines, including natural language processing, network analysis, machine learning and deep learning, so there are many opportunities for further research. Of particular interest to us is whether we can predict the emergence of new topics in social media, and how does the engagement of users to their topics of interest change over time.
Are you involved in collaborative research?
Our research has connections to other groups within UTS which has helped in forming new ideas, such as the Network Science lab within the Advanced Analytics Institute.
What inspired you to undertake a PhD in computer science?
I was working as a Data Scientist in industry and wanted to gain a much deeper understanding of new and emerging methods in machine learning. I had been learning some techniques through my work and also on the side through online courses and small projects, but wanted to further develop and apply my own ideas. A PhD in computer science has been a great way to gain the technical depth I desired and add my own contributions to the field.