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
2020
Journal Article
Computational prediction of protein ubiquitination sites mapping on Arabidopsis thaliana
Mosharaf, Md. Parvez, Hassan, Md. Mehedi, Ahmed, Fee Faysal, Khatun, Mst. Shamima, Moni, Mohammad Ali and Mollah, Md. Nurul Haque (2020). Computational prediction of protein ubiquitination sites mapping on Arabidopsis thaliana. Computational Biology and Chemistry, 85 107238, 107238. doi: 10.1016/j.compbiolchem.2020.107238
2020
Journal Article
Network-based computational approach to identify genetic links between cardiomyopathy and its risk factors
Haidar, M. Nasim, Islam, M. Babul, Chowdhury, Utpala Nanda, Rahman, M. Rezanur, Huq, Fazlul, Quinn, Julian M.W. and Moni, Mohammad Ali (2020). Network-based computational approach to identify genetic links between cardiomyopathy and its risk factors. IET Systems Biology, 14 (2), 75-84. doi: 10.1049/iet-syb.2019.0074
2020
Journal Article
Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer's disease: Insights from a systems biomedicine perspective
Rahman, Md. Rezanur, Islam, Tania, Zaman, Toyfiquz, Shahjaman, Md., Karim, Md. Rezaul, Huq, Fazlul, Quinn, Julian M.W., Holsinger, R.M. Damian, Gov, Esra and Moni, Mohammad Ali (2020). Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer's disease: Insights from a systems biomedicine perspective. Genomics, 112 (2), 1290-1299. doi: 10.1016/j.ygeno.2019.07.018
2020
Journal Article
Network-based genetic profiling reveals cellular pathway differences between follicular thyroid carcinoma and follicular thyroid adenoma
Hossain, Md. Ali, Asa, Tania Akter, Rahman, Md. Mijanur, Uddin, Shahadat, Moustafa, Ahmed A., Quinn, Julian M. W. and Moni, Mohammad Ali (2020). Network-based genetic profiling reveals cellular pathway differences between follicular thyroid carcinoma and follicular thyroid adenoma. International Journal of Environmental Research and Public Health, 17 (4) 1373, 1373. doi: 10.3390/ijerph17041373
2020
Journal Article
A network-based bioinformatics approach to identify molecular biomarkers for type 2 diabetes that are linked to the progression of neurological diseases
Rahman, Md Habibur, Peng, Silong, Hu, Xiyuan, Chen, Chen, Rahman, Md Rezanur, Uddin, Shahadat, Quinn, Julian M. W. and Moni, Mohammad Ali (2020). A network-based bioinformatics approach to identify molecular biomarkers for type 2 diabetes that are linked to the progression of neurological diseases. International Journal of Environmental Research and Public Health, 17 (3) 1035, 1-25. doi: 10.3390/ijerph17031035
2020
Journal Article
A framework to understand the progression of cardiovascular disease for type 2 diabetes mellitus patients using a network approach
Hossain, Md Ekramul, Uddin, Shahadat, Khan, Arif and Moni, Mohammad Ali (2020). A framework to understand the progression of cardiovascular disease for type 2 diabetes mellitus patients using a network approach. International Journal of Environmental Research and Public Health, 17 (2) 596, 596. doi: 10.3390/ijerph17020596
2020
Journal Article
A systems biology approach to identifying genetic factors affected by aging, lifestyle factors, and type 2 diabetes that influences Parkinson's disease progression
Sakib, Najmus, Chowdhury, Utpala Nanda, Islam, M. Babul, Ahmad, Shamim and Moni, Mohammad Ali (2020). A systems biology approach to identifying genetic factors affected by aging, lifestyle factors, and type 2 diabetes that influences Parkinson's disease progression. Informatics in Medicine Unlocked, 21 100448, 100448. doi: 10.1016/j.imu.2020.100448
2020
Journal Article
Network-based computational approach to identify delineating common cell pathways influencing type 2 diabetes and diseases of bone and joints
Moni, Mohammad Ali, Islam, M. Babul, Rahman, Md Rezanur, Rashed-Al-Mahfuz, Md, Awal, Md Abdul, Islam, Sheikh Mohammed Shariful, Mollah, Md. Nurul Haque and Quinn, Julian M. W. (2020). Network-based computational approach to identify delineating common cell pathways influencing type 2 diabetes and diseases of bone and joints. IEEE Access, 8 8941110, 1486-1497. doi: 10.1109/ACCESS.2019.2962091
2020
Journal Article
Network-based identification of genetic factors in ageing, lifestyle and type 2 diabetes that influence to the progression of Alzheimer's disease
Chowdhury, Utpala Nanda, Islam, M. Babul, Ahmad, Shamim and Moni, Mohammad Ali (2020). Network-based identification of genetic factors in ageing, lifestyle and type 2 diabetes that influence to the progression of Alzheimer's disease. Informatics in Medicine Unlocked, 19 100309, 100309. doi: 10.1016/j.imu.2020.100309
2020
Journal Article
Systems biology and bioinformatics approach to identify gene signatures, pathways and therapeutic targets of Alzheimer's disease
Chowdhury, Utpala Nanda, Islam, M. Babul, Ahmad, Shamim and Moni, Mohammad Ali (2020). Systems biology and bioinformatics approach to identify gene signatures, pathways and therapeutic targets of Alzheimer's disease. Informatics in Medicine Unlocked, 21 100439, 100439. doi: 10.1016/j.imu.2020.100439
2020
Book Chapter
Methods for the analysis of micro-pollutants
Ahmed, M. B., Johir, M. A.H., Ngo, Huu Hao, Guo, Wenshan, Zhou, J. L., Belhaj, D. and Moni, M. A. (2020). Methods for the analysis of micro-pollutants. Current Developments in Biotechnology and Bioengineering: Emerging Organic Micro-pollutants. (pp. 63-86) Amsterdam, Netherlands: Elsevier. doi: 10.1016/B978-0-12-819594-9.00004-8
2020
Journal Article
Identification of the core ontologies and signature genes of polycystic ovary syndrome (PCOS): a bioinformatics analysis
Islam, Md Rakibul, Ahmed, Md Liton, Kumar Paul, Bikash, Bhuiyan, Touhid, Ahmed, Kawsar and Moni, Mohammad Ali (2020). Identification of the core ontologies and signature genes of polycystic ovary syndrome (PCOS): a bioinformatics analysis. Informatics in Medicine Unlocked, 18 100304, 100304. doi: 10.1016/j.imu.2020.100304
2019
Journal Article
Comparing different supervised machine learning algorithms for disease prediction
Uddin, Shahadat, Khan, Arif, Hossain, Md Ekramul and Moni, Mohammad Ali (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making, 19 (1) 281. doi: 10.1186/s12911-019-1004-8
2019
Journal Article
Bioinformatics methodologies to identify interactions between Type 2 diabetes and neurological comorbidities
Rahman, Md Habibur, Peng, Silong, Hu, Xiyuan, Chen, Chen, Uddin, Shahadat, Quinn, Julian M. W. and Moni, Mohammad Ali (2019). Bioinformatics methodologies to identify interactions between Type 2 diabetes and neurological comorbidities. IEEE Access, 7 8933380, 183948-183970. doi: 10.1109/ACCESS.2019.2960037
2019
Conference Publication
Comorbidity effects of mitochondrial dysfunction to the progression of neurological disorders: Insights from a systems biomedicine perspective
Satu, Md. Shahriare, Chandra Howlader, Koushik, Niamat Ullah Akhund, Tajim Md., Quinn, Julian M.W., Lio, Pietro and Moni, Mohammad Ali (2019). Comorbidity effects of mitochondrial dysfunction to the progression of neurological disorders: Insights from a systems biomedicine perspective. 2019 22nd International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 18-20 December 2019. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICCIT48885.2019.9038388
2019
Journal Article
Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality
Hossain, Md. Ali, Saiful Islam, Sheikh Muhammad, Quinn, Julian M.W., Huq, Fazlul and Moni, Mohammad Ali (2019). Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality. Journal of Biomedical Informatics, 100 103313, 103313. doi: 10.1016/j.jbi.2019.103313
2019
Journal Article
Machine learning-based models for early stage detection of autism spectrum disorders
Akter, Tania, Shahriare Satu, Md., Khan, Md. Imran, Ali, Mohammad Hanif, Uddin, Shahadat, Lio, Pietro, Quinn, Julian M.W. and Moni, Mohammad Ali (2019). Machine learning-based models for early stage detection of autism spectrum disorders. IEEE Access, 7 8895818, 166509-166527. doi: 10.1109/ACCESS.2019.2952609
2019
Journal Article
Mapping 123 million neonatal, infant and child deaths between 2000 and 2017
Burstein, Roy, Henry, Nathaniel J., Collison, Michael L., Marczak, Laurie B., Sligar, Amber, Watson, Stefanie, Marquez, Neal, Abbasalizad-Farhangi, Mahdieh, Abbasi, Masoumeh, Abd-Allah, Foad, Abdoli, Amir, Abdollahi, Mohammad, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abrigo, Michael R. M., Acharya, Dilaram, Adebayo, Oladimeji M., Adekanmbi, Victor, Adham, Davoud, Afshari, Mahdi, Aghaali, Mohammad, Ahmadi, Keivan, Ahmadi, Mehdi, Ahmadpour, Ehsan, Ahmed, Rushdia, Akal, Chalachew Genet, Akinyemi, Joshua O., Alahdab, Fares, Alam, Noore ... Hay, Simon I. (2019). Mapping 123 million neonatal, infant and child deaths between 2000 and 2017. Nature, 574 (7778), 353-358. doi: 10.1038/s41586-019-1545-0
2019
Journal Article
A computational approach to identify blood cell-expressed Parkinson's disease biomarkers that are coordinately expressed in brain tissue
Moni, Mohammad Ali, Rana, Humayan Kabir, Islam, M. Babul, Ahmed, Mohammad Boshir, Xu, Haoming, Hasan, Md Al Mehedi, Lei, Yiming and Quinn, Julian M.W. (2019). A computational approach to identify blood cell-expressed Parkinson's disease biomarkers that are coordinately expressed in brain tissue. Computers in Biology and Medicine, 113 103385, 103385. doi: 10.1016/j.compbiomed.2019.103385
2019
Journal Article
The relationship between fat mass and obesity-associated gene polymorphism and obesity among children in China: a systematic review and meta-analysis
Dong, Zhiyong, Islam, Sheikh Mohammed Shariful, Yu, Ashley M., Razi, Faraz, Gupta, Ramit Kumar, Moni, Mohammad Ali and Wang, Cunchuan (2019). The relationship between fat mass and obesity-associated gene polymorphism and obesity among children in China: a systematic review and meta-analysis. International Journal of Noncommunicable Diseases, 4 (4), 104-114. doi: 10.4103/jncd.jncd_43_19
Supervision
Availability
- Dr Mohammad Ali Moni is:
- Available for supervision
Looking for a supervisor? Read our advice on how to choose a supervisor.
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
Developing AI-based Discission Support System utilising multimodal data
Principal Advisor
Other advisors: Associate Professor Asaduzzaman Khan
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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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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
Wearable devices and AI Models for Monitoring, Predicting and Assessment Post-stroke Recovery
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
Completed supervision
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2025
Doctor Philosophy
Understanding the pathophysiology of stroke using bioinformatics and statistical genetics
Principal Advisor
Other advisors: Associate Professor Asaduzzaman Khan, Dr Jian Zeng
Media
Enquiries
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