Overview
Background
Dr. Sebastiano Barbieri is Associate Professor and Principal Research Fellow at the Queensland Digital Health Centre, University of Queensland (UQ) and Adjunct Associate Professor at the Centre for Big Data Research in Health, University of New South Wales (UNSW). His work lies at the intersection of machine learning and healthcare, where he develops innovative computational methods to tackle pressing challenges in medicine.
Aiming to improve patient outcomes and streamline clinical workflows, Dr. Barbieri develops machine learning models tailored to real-world healthcare applications. His current research spans risk prediction using electronic medical records, medical image processing, and the safe and effective integration of AI into clinical decision-making processes.
A strong advocate for responsible AI in healthcare, Dr. Barbieri champions the use of emerging technologies such as synthetic data generation and federated learning. These approaches not only enhance data accessibility and privacy but also accelerate the development of robust, data-driven solutions for digital health.
Availability
- Associate Professor Sebastiano Barbieri is:
- Available for supervision
Qualifications
- Bachelor of Mathematics, Universität des Saarlandes
- Masters (Coursework) of Image Processing, Universität des Saarlandes
- Doctor of Philosophy of Computer Science, Jacobs University
- Masters (Coursework) of Biostatistics, Macquarie University
Works
Search Professor Sebastiano Barbieri’s works on UQ eSpace
2017
Journal Article
Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI
Barbieri, Sebastiano, Brönnimann, Michael, Boxler, Silvan, Vermathen, Peter and Thoeny, Harriet C. (2017). Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI. European Radiology, 27 (4), 1547-1555. doi: 10.1007/s00330-016-4449-5
Supervision
Availability
- Associate Professor Sebastiano Barbieri is:
- Available for supervision
Looking for a supervisor? Read our advice on how to choose a supervisor.
Available projects
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Machine learning for the generation and distribution of synthetic electronic medical records (EMRs) representative of the Australian population
This project will develop a novel software and data platform, comprising nationally representative synthetic EMR data, to enable safe and ethical Australian innovation in clinical artificial intelligence.
Supervision history
Current supervision
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Doctor Philosophy
Machine learning for generation and distribution of synthetic electronic medical records (EMRs) representative of the Australian population
Principal Advisor
Other advisors: Dr Guanglin Zhou
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Doctor Philosophy
NINA national infrastructure for digital health
Principal Advisor
Other advisors: Professor Clair Sullivan
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Master Philosophy
Investigating the Impact of Artificial Intelligence on Clinical Workflows, Efficiency, and Health Economics: Implementation Insights, Bias Evaluation, and Strategic Mitigation strategies for Clinical AI Integration
Principal Advisor
Other advisors: Professor Ian Scott, Dr Anton van Der Vegt
Media
Enquiries
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