Overview
Background
Dr. Sebastiano Barbieri is A/Prof and principal research fellow at the Queensland Digital Health Centre (UQ) and adjunct A/Prof at the Centre for Big Data Research in Health (UNSW). Sebastiano is interested in developing novel machine learning methods and in applying these techniques to various problems in health and medicine, with the aim of improving patient care, making clinical processes more streamlined and effective, and improving population health.
Sebastiano completed his PhD in computer science at the Fraunhofer MeVis Institute for Digital Medicine and Jacobs University Bremen, Germany and obtained Master's degrees in visual computing (Saarland University, Germany) and biostatistics (Biostatistics Collaboration of Australia).
His current research focuses on deep learning for risk prediction based on electronic medical records, synthetic data generation, federated learning, and medical image processing.
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
2025
Journal Article
Machine learning cluster analysis identifies increased 12-month mortality risk in transcatheter aortic valve replacement recipients
Meredith, Thomas, Mohammed, Farhan, Pomeroy, Amy, Barbieri, Sebastiano, Meijering, Erik, Jorm, Louisa, Roy, David, Kovacic, Jason, Feneley, Michael, Hayward, Christopher, Muller, David and Namasivayam, Mayooran (2025). Machine learning cluster analysis identifies increased 12-month mortality risk in transcatheter aortic valve replacement recipients. Frontiers in Cardiovascular Medicine, 12 1444658. doi: 10.3389/fcvm.2025.1444658
2024
Journal Article
Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project
Kuo, Nicholas I-Hsien, Perez-Concha, Oscar, Hanly, Mark, Mnatzaganian, Emmanuel, Hao, Brandon, Di Sipio, Marcus, Yu, Guolin, Vanjara, Jash, Valerie, Ivy Cerelia, de Oliveira Costa, Juliana, Churches, Timothy, Lujic, Sanja, Hegarty, Jo, Jorm, Louisa and Barbieri, Sebastiano (2024). Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project. JMIR Medical Education, 10 (1) e51388, e51388. doi: 10.2196/51388
2021
Journal Article
Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients
Kaandorp, Misha P. T., Barbieri, Sebastiano, Klaassen, Remy, van Laarhoven, Hanneke W. M., Crezee, Hans, While, Peter T., Nederveen, Aart J. and Gurney-Champion, Oliver J. (2021). Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients. Magnetic Resonance in Medicine, 86 (4), 2250-2265. doi: 10.1002/mrm.28852
Supervision
Availability
- Associate Professor Sebastiano Barbieri is:
- Available for supervision
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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
NINA national infrastructure for digital health
Principal Advisor
Other advisors: Professor Clair Sullivan
Media
Enquiries
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