Overview
Background
Dr Moni holds a PhD in Artificial Intelligence & Data Science in 2014 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 also worked as an assistant professor and lecturer in two universities (PUST and JKKNIU) from 2007 to 2011. 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 (MRI, fMRI, Ultrasound, X-Ray), 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.
Availability
- Dr Mohammad Ali Moni is:
- Available for supervision
Fields of research
Qualifications
- Doctor of Philosophy, University of Cambridge
Research interests
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Artificial Intelligence, Computer Vision, Machine Learning, Deep-Learning
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Medical Imaging, Medical Image Analysis, Neuro Imaging
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Digital Health, Data Science, Health Informatics, Clinical Informatics
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Data Mining, Text Mining, Natural Language Processing
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Bioinformatics, Systems Biology, Computational Biology
Research impacts
During the last 5 years he has puvblished over 200 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 (>12000 citations, i10-index 157 and an h-index of 50 according to Google Scholar), received $1.89 M as CI-A (8.9 M total) 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 ($50,000), 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
Works
Search Professor Mohammad Ali Moni’s works on UQ eSpace
2015
Journal Article
CytoCom: A Cytoscape app to visualize, query and analyse disease comorbidity networks
Moni, Mohammad Ali, Xu, Haoming and Liò, Pietro (2015). CytoCom: A Cytoscape app to visualize, query and analyse disease comorbidity networks. Bioinformatics, 31 (6), 969-971. doi: 10.1093/bioinformatics/btu731
2014
Journal Article
Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies
Moni, Mohammad Ali and Liò, Pietro (2014). Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies. BMC Bioinformatics, 15 (1) 333. doi: 10.1186/1471-2105-15-333
2014
Journal Article
comoR: A software for disease comorbidity risk assessment
Moni, Mohammad Ali and Liò, Pietro (2014). comoR: A software for disease comorbidity risk assessment. Journal of Clinical Bioinformatics, 4 (1) 8. doi: 10.1186/2043-9113-4-8
2013
Conference Publication
Comparing viral (HIV) and bacterial (staphylococcus aureus) infection of the bone tissue
Moni, Mohammad Ali, Liò, Pietro and Milanesi, Luciano (2013). Comparing viral (HIV) and bacterial (staphylococcus aureus) infection of the bone tissue. BIOINFORMATICS 2013 - International Conference on Bioinformatics Models, Methods and Algorithms, Barcelona, Spain, 11-14 February 2013. Setúbal, Portugal: SciTePress.
2012
Journal Article
Modelling osteomyelitis
Liò, Pietro, Paoletti, Nicola, Moni, Mohammad A., Atwell, Kathryn, Merelli, Emanuela and Viceconti, Marco (2012). Modelling osteomyelitis. BMC Bioinformatics, 13 (s1) S12. doi: 10.1186/1471-2105-13-S14-S12
Supervision
Availability
- Dr Mohammad Ali Moni is:
- Available for supervision
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Available projects
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Deep learning models development and application to the Neuro Imaging (MRI and fMRI)
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.
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Deep Leaning Model to identify Neuroimaging biomarkers
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Deep Learning models to solve inverse problems utiling MRI/fMRI image
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AI-based based model development for Magnetic Resonance Imaging
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AI-based Model development for ECG/EEG study
Supervision history
Current supervision
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Doctor Philosophy
Coloured noise estimation using electroencephalogram data and deep-learning method for improvement of cognitive function
Principal Advisor
Other advisors: Associate Professor Asaduzzaman Khan
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Doctor Philosophy
Understanding the pathophysiology of stroke using bioinformatics and statistical genetics
Principal Advisor
Other advisors: Associate Professor Asaduzzaman Khan, Dr Jian Zeng
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Doctor Philosophy
Wearable devices and AI Models for Monitoring, Predicting and Assessment Post-stroke Recovery
Principal Advisor
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Doctor Philosophy
Developing AI-based Discission Support System utilising multimodal data
Principal Advisor
Other advisors: Associate Professor Asaduzzaman Khan
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Doctor Philosophy
Understanding the pathophysiology of stroke using bioinformatics and statistical genetics approaches
Principal Advisor
Other advisors: Associate Professor Asaduzzaman Khan, Dr Jian Zeng
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Doctor Philosophy
Understanding the pathophysiology of stroke using bioinformatics and statistical genetics
Principal Advisor
Other advisors: Associate Professor Asaduzzaman Khan, Dr Jian Zeng
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Doctor Philosophy
Robust and Explainable AI to Solve Clinical Problems
Principal Advisor
Other advisors: Associate Professor Asaduzzaman Khan
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Master Philosophy
Advancing Maternal-Fetal Health in Underserved Communities: A Computer Vision Approach
Principal Advisor
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Doctor Philosophy
Managing non-communicable diseases (NCDs) to achieve Universal Health Coverage (UHC) in South Asia: A case study from Bangladesh
Associate Advisor
Other advisors: Associate Professor Asaduzzaman Khan
Media
Enquiries
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