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I am a health data scientist focused on improving healthcare processes and contributing to better patient outcomes. I specialise in analysing and extracting meaningful information from large unstructured health datasets using artificial intelligence (AI) and natural language processing (NLP). I currently lead research applying large language models (LLMs) and explainable AI to clinical text, with a focus on three key applications: improving the accuracy and efficiency of clinical coding, generating concise clinical summaries, and extracting clinically relevant information to support patient care.

I have a strong interest in teaching, learning, and the dissemination of AI in health. I founded and currently co-host the , (established in 2018) and co-organise the , both initiatives aimed at fostering interdisciplinary collaboration. I also teach and convene three postgraduate courses on applied AI in health, as part of the Master of Health Data Science and Clinical AI programs. I designed and created two of these courses: Health Data Analytics: Machine Learning I (HDAT9500), which introduces machine learning techniques for health data science research questions, and Health Data Analytics: Machine Learning II (HDAT9510), which focuses on deep learning methods for solving data science problems in healthcare.