I obtained my PhD at the Centre for Big Data Research in Health (CBDRH), University of New South Wales (UNSW), Australia. I currently work as a Research Fellow at CBDRH, UNSW Sydney, and as a Data Scientist at The George Institute for Global Health (TGI).

My research focuses on the application of natural language processing (NLP) and deep learning to extract information from unstructured health data, particularly clinical notes. Key areas of interest include the de-identification and clinical coding of free-text data from electronic medical records (EMRs), as well as predictive modelling using brain imaging data with convolutional neural networks and transformer-based architectures.

I developed an end-to-end deep learning-based named entity recognition framework that effectively removes personally identifiable information (PII) from free-text health data. This framework has been used to de-identify clinical notes in the Cardiac Analytics and Innovation (CardiacAI) project. I have also investigated transformer-based models for clinical coding tasks.

Currently, I am involved in the Next-generation Clinical Registry project at CBDRH and lead the machine learning model development for the Outcome PredicTion in IntraCerebral Hemorrhage Study (OPTICS) at TGI.

With strong machine learning expertise, a solid background in software engineering, and experience in big data analysis and data management, I bring a unique and valuable combination of skills to the field of health data science.