Dr Mohammad Ali Moni

Senior Research Fellow

School of Health and Rehabilitation Sciences
Faculty of Health and Behavioural Sciences

Overview

Dr Moni holds a PhD in Artificial Intelligence & Digital Health Data Science in 2015 from the University of Cambridge, UK followed by postdoctoral training at the University of New South Wales, University of Sydney Vice-chancellor fellowship, and Senior Data Scientist at the University of Oxford. Dr Moni then joined UQ in 2021. He is an Artificial Intelligence, Computer Vision & Machine learning, Digital Health Data Science, Health Informatics and Bioinformatics researcher developing interpretable and clinical applicable machine learning and deep learning models to increase the performance and transparency of AI-based automated decision-making systems.

His research interests include quantifying and extracting actionable knowledge from data to solve real-world problems and giving humans explainable AI models through feature visualisation and attribution methods. He has applied these techniques to various multi-disciplinary applications such as medical imaging including stroke MRI/fMRI imaging, real-time cancer imaging. He led and managed significant research programs in developing machine-learning, deep-learning and translational data science models, and software tools to aid the diagnosis and prediction of disease outcomes, particularly for hard-to-manage complex and chronic diseases. His research interest also includes developing Data Science, machine learning and deep learning algorithms, models and software tools utilising different types of data, especially medical images, neuroimaging, EEG, ECG, Bioinformatics, and secondary usage of routinely collected data.

  • I am currently recruiting graduate students. Check out Available Projects for details. Open to both Domestic and International students.

Research Interests

  • Artificial Intelligence, Computer Vision, Machine Learning, Deep-Learning
  • Medical Imaging, Medical Image Analysis, Neuro Imaging
  • Digital Health, Data Science, Health Informatics, Clinical Informatics
  • Data Mining, Text Mining, Natural Language Processing
  • Bioinformatics, Systems Biology, Computational Biology

Research Impacts

During the last 5 years he has puvblished over 150 journal articles in many top tier journals including The Lancet, Jama Oncology. The impact of his research is evidenced by the high number of citations to his work (>3300 citations and an h-index of 29 according to Google Scholar) and awards including :

  • Best Impact Award in International Conference on Applied Intelligence and Informatics, UK July 30-31, 2021
  • University of Wollongong Engineering & information science Distinguished Early Career Fellowship.2019-2020
  • Certara-Monash Fellowship Awarded ($2,00,000), Certara Australia Pty. Ltd, 2019
  • Seed funding from two companies Karte Ltd (Japan) and iHealthOmics Ltd (Hong Kong) to develop AI-based health-care related software products. Received seed funding ($40,000) from Karte Ltd. 2018-2020
  • USyd DVC Research Fellowship ($3,80000), University of Sydney2017-2020
  • The Ridley Ken Davies Award ($50,000)-- utilising the research data obtained through Dubbo Osteoporosis Epidemiological Study, Ridley Corporation, Australia 2016
  • Travel award to attend ANZBMS Conference, Australia, 2016
  • Best student paper award in international conference- IDBSS2014, UK2014
  • Travel award to attend NIMBioS Modeling, University of Tennessee, USA. 2013
  • The Cambridge Commonwealth, European & International Trust award, The Commonwealth Trust, UK 2011

Qualifications

  • Doctor of Philosophy, University of Cambridge

Publications

View all Publications

Supervision

  • Doctor Philosophy

View all Supervision

Available Projects

  • Magnetic resonance (MR) imaging has become an important non-invasive radiological modality for various clinical applications, such as stoke and cancer. Extracting meaningful clinical information without human interaction is a challenging task. Developing such automatic methods are important in order to reduce human errors and the time taken by clinicians.

    In this project, the student will develop novel deep learning algorithms to solve segmentation and detection problems from imaging that could possibly be deployed to MRI & fMRI scanners and may eventually used for diagnostic purposes. The project will involve applying computer vision and deep learning techniques to MR image processing and analysis.

View all Available Projects

Publications

Book Chapter

  • Alom, Zulfikar, Azim, Mohammad Abdul, Aung, Zeyar, Khushi, Matloob, Car, Josip and Moni, Mohammad Ali (2022). Early Stage Detection of Heart Failure Using Machine Learning Techniques. Lecture Notes on Data Engineering and Communications Technologies. (pp. 75-88) Singapore: Springer Science and Business Media Deutschland GmbH. doi: 10.1007/978-981-16-6636-0_7

  • Mahmud, Mufti, Kaiser, M. Shamim, Rahman, Muhammad Arifur, Wadhera, Tanu, Brown, David J., Shopland, Nicholas, Burton, Andrew, Hughes-Roberts, Thomas, Mamun, Shamim Al, Ieracitano, Cosimo, Tania, Marzia Hoque, Moni, Mohammad Ali, Islam, Mohammed Shariful, Ray, Kanad and Hossain, M. Shahadat (2022). Towards Explainable and Privacy-Preserving Artificial Intelligence for Personalisation in Autism Spectrum Disorder. Universal Access in Human-Computer Interaction. User and Context Diversity. (pp. 356-370) Cham: Springer International Publishing. doi: 10.1007/978-3-031-05039-8_26

  • Satu, Md. Shahriare, Mizan, K. Shayekh Ebne, Jerin, Syeda Anika, Whaiduzzaman, Md, Barros, Alistair, Ahmed, Kawsar and Moni, Mohammad Ali (2021). COVID-Hero: Machine Learning Based COVID-19 Awareness Enhancement Mobile Game for Children. Applied Intelligence and Informatics. (pp. 321-335) Cham: Springer International Publishing. doi: 10.1007/978-3-030-82269-9_25

Journal Article

Conference Publication

PhD and MPhil Supervision

Current Supervision

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Magnetic resonance (MR) imaging has become an important non-invasive radiological modality for various clinical applications, such as stoke and cancer. Extracting meaningful clinical information without human interaction is a challenging task. Developing such automatic methods are important in order to reduce human errors and the time taken by clinicians.

    In this project, the student will develop novel deep learning algorithms to solve segmentation and detection problems from imaging that could possibly be deployed to MRI & fMRI scanners and may eventually used for diagnostic purposes. The project will involve applying computer vision and deep learning techniques to MR image processing and analysis.