Associate Professor Sebastiano Barbieri
Associate Professor

Sebastiano Barbieri

Email: 

Background

Dr. Sebastiano Barbieri is an 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

Search Professor Sebastiano Barbieri’s works on UQ eSpace

5 works between 2020 and 2025
All (5) Journal Article (5)

1 - 5 of 5 works

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

Machine learning cluster analysis identifies increased 12-month mortality risk in transcatheter aortic valve replacement recipients

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, e51388-10. doi: 10.2196/51388

Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project

2023

Journal Article

Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: example using antiretroviral therapy for HIV

Kuo, Nicholas I-Hsien, Garcia, Federico, Sönnerborg, Anders, Böhm, Michael, Kaiser, Rolf, Zazzi, Maurizio, Polizzotto, Mark, Jorm, Louisa and Barbieri, Sebastiano (2023). Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: example using antiretroviral therapy for HIV. Journal of Biomedical Informatics, 144 104436. doi: 10.1016/j.jbi.2023.104436

Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: example using antiretroviral therapy for HIV

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

Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients

2020

Journal Article

Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI

Barbieri, Sebastiano, Gurney-Champion, Oliver J., Klaassen, Remy and Thoeny, Harriet C. (2020). Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI. Magnetic Resonance in Medicine, 83 (1), 312-321. doi: 10.1002/mrm.27910

Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI

Availability

Associate Professor Sebastiano Barbieri is:
Available for supervision

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Available projects

Supervision history

Current supervision

  • Doctor Philosophy

    NINA national infrastructure for digital health

    Principal Advisor

    Other advisors: Professor Clair Sullivan

Enquiries

For media enquiries about Associate Professor Sebastiano Barbieri's areas of expertise, story ideas and help finding experts, contact our Media team:

communications@uq.edu.au