Keywords
Biography
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...view more
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 Machine Learning in Health Club, (established in 2018) and co-organise the UNSW Health Data Science Datathon, 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.
My Qualifications
PhD Artificial Intelligence, Computer Science, Universidad Carlos III de Madrid, Spain, 2008
My Research Supervision
Areas of supervision
I am currently supervising:
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- Three PhD students (two as primary supervisor, one as secondary supervisor)
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- Three Master of Health Data Science students (two as primary supervisor, one as secondary supervisor)
I have previously supervised to completion:
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- Two PhD students (as joint supervisor)
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- Eight Master of Health Data Science students (six as primary supervisor, two as secondary supervisor)
My Teaching
As part of of the MSc Health Data Science Program and the Clinical AI Program, I currently convene and teach the courses:
1. Health Data Analytics: Machine Learning I (HDAT9500) [Link]
Course Summary: This course provides an introduction to machine learning methods for addressing health research questions. You will learn how to formulate relevant questions and frame them as machine learning problems within a healthcare context. The course covers the fundamentals of key methods, including supervised learning (classification and regression) and unsupervised learning (dimensionality reduction and clustering). With a strong hands-on focus, the course equips you with the foundational skills and understanding needed to apply these methods to real-world healthcare data challenges.
2. Health Data Analytics: Machine Learning II (HDAT9510) [Link]
Course Summary: In this course, which builds upon a foundational understanding of machine learning, you will learn the core theory and practical application of deep learning algorithms in the area of health. Adopting a hands-on approach, the course is tailored to provide you with the essential skills and knowledge needed to effectively tackle and resolve healthcare data challenges using the deep learning algorithms covered in the course.
3. Health Data Science: Capstone (HDAT9910) [Link]
Course Summary: The capstone project involves completing extensive, desk-based, independent research tasks, requiring the use of the R and/or Python programming languages. An entire health data science project has been constructed and sliced into the respective stages of the health data science pipeline, which you are required to complete.
Location
Follow
Publications
ORCID as entered in ROS
https://orcid.org/0000-0002-8823-7090