Research
I work in multidisciplinary teams to develop and apply AI/ML, as well as mathematical and computational technologies, to biology and health. Rapid advances in areas like machine learning-based artificial intelligence (AI) means that we have a greater and more powerful range of tools than ever before to support scientific advances in biology and translate this to impact in areas like personalised medicine.
I recently joined the University of Melbourne (School of Mathematics & Statistics) as a Research Fellow in Biological Data Science, where I'm working within Melbourne Integrative Genomics (MIG) and the ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems (MACSYS) to advance AI-related research. I am working with Dr. Heejung Shim (Shim Lab) on statistical and machine learning methods to analyse complex and large-scale biological data.
My PhD research developed probabilistic machine learning and deep learning methods for personalised medicine applications, aiming to address how to meet the statistical inference needs of individual-level analyses, while effectively utilising the power of large datasets that capture complex relationships explaining patient outcomes. My PhD was advised by Prof. Samuel Kaski (Aalto University, University of Manchester) at the Finnish Center for Artificial Intelligence (FCAI) and Probabilistic Machine Learning (PML) group at Aalto University, in collaboration with the INTERVENE consortium and Institute for Molecular Medicine Finland (FIMM). This work made extensive use of large-scale health and biological data sources (genetic biobanks, population-scale health registers, electronic health records, etc.), including the FinRegistry, UK Biobank and FinnGen datasets.
I started my research career at the University of Sydney (School of Mathematics and Statistics), where I received First Class Honours in Applied Mathematics and worked with Prof. Eduardo Altmann and Dr. Lamiae Azizi on complex systems and network science research.