Dr Mohammad Ali Moni
Dr

Mohammad Ali Moni

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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

Applications in life sciences Applied computing Artificial intelligence Biological Sciences Biomedical and Clinical Sciences Biomedical engineering Cardiovascular medicine and haematology Clinical sciences Cognitive and computational psychology Computer vision and multimedia computation Cybersecurity and privacy Data management and data science Distributed computing and systems software Engineering Health Sciences Human-centred computing Information and Computing Sciences Informetrics Library and information studies Machine learning Medical biotechnology Mobile computing Nanotechnology Neurosciences Other engineering Psychology Sports science and exercise

Qualifications

  • Doctor of Philosophy, University of Cambridge

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 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

Search Professor Mohammad Ali Moni’s works on UQ eSpace

315 works between 2012 and 2024
All (315) Journal Article (274) Book Chapter (4) Conference Publication (36) Book (1)

1 - 20 of 315 works

2024

Journal Article

Correction to: A computational approach to design a polyvalent vaccine against human respiratory syncytial virus (Scientific Reports, (2023), 13, 1, (9702), 10.1038/s41598-023-35309-y)

Moin, Abu Tayab, Ullah, Md. Asad, Patil, Rajesh B., Faruqui, Nairita Ahsan, Araf, Yusha, Das, Sowmen, Uddin, Khaza Md. Kapil, Hossain, Md. Shakhawat, Miah, Md. Faruque, Moni, Mohammad Ali, Chowdhury, Dil Umme Salma and Islam, Saiful (2024). Correction to: A computational approach to design a polyvalent vaccine against human respiratory syncytial virus (Scientific Reports, (2023), 13, 1, (9702), 10.1038/s41598-023-35309-y). Scientific Reports, 14 (1) 15721. doi: 10.1038/s41598-024-66721-7

Correction to: A computational approach to design a polyvalent vaccine against human respiratory syncytial virus (Scientific Reports, (2023), 13, 1, (9702), 10.1038/s41598-023-35309-y)

2024

Journal Article

Advancing thyroid care: An accurate trustworthy diagnostics system with interpretable AI and hybrid machine learning techniques

Sutradhar, Ananda, Akter, Sharmin, Shamrat, F M Javed Mehedi, Ghosh, Pronab, Zhou, Xujuan, Idris, Mohd Yamani Idna Bin, Ahmed, Kawsar and Moni, Mohammad Ali (2024). Advancing thyroid care: An accurate trustworthy diagnostics system with interpretable AI and hybrid machine learning techniques. Heliyon, 10 (17) e36556. doi: 10.1016/j.heliyon.2024.e36556

Advancing thyroid care: An accurate trustworthy diagnostics system with interpretable AI and hybrid machine learning techniques

2024

Journal Article

Trends and levels of the global, regional, and national burden of appendicitis between 1990 and 2021: findings from the Global Burden of Disease Study 2021

Han, Hannah, Letourneau, Ian D, Abate, Yohannes Habtegiorgis, Abdelmasseh, Michael, Abu-Gharbieh, Eman, Adane, Tigist Demssew, Ahinkorah, Bright Opoku, Ahmad, Aqeel, Ahmadi, Ali, Ahmed, Ayman, Alhalaiqa, Fadwa Naji, Al-Sabah, Salman Khalifah, Al-Worafi, Yaser Mohammed, Amu, Hubert, Andrei, Catalina Liliana, Anoushiravani, Amir, Arabloo, Jalal, Aravkin, Aleksandr Y, Ashraf, Tahira, Azadnajafabad, Sina, Baghcheghi, Nayereh, Bagherieh, Sara, Bantie, Berihun Bantie, Bardhan, Mainak, Basile, Guido, Bayleyegn, Nebiyou Simegnew, Behnoush, Amir Hossein, Bekele, Alehegn, Bhojaraja, Vijayalakshmi S ... Dirac, M Ashworth (2024). Trends and levels of the global, regional, and national burden of appendicitis between 1990 and 2021: findings from the Global Burden of Disease Study 2021. The Lancet Gastroenterology and Hepatology, 9 (9), 825-858. doi: 10.1016/S2468-1253(24)00157-2

Trends and levels of the global, regional, and national burden of appendicitis between 1990 and 2021: findings from the Global Burden of Disease Study 2021

2024

Journal Article

Global, regional, and national burden of gout, 1990–2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021

Cross, Marita, Ong, Kanyin Liane, Culbreth, Garland T, Steinmetz, Jaimie D, Cousin, Ewerton, Lenox, Hailey, Kopec, Jacek A, Haile, Lydia M, Brooks, Peter M, Kopansky-Giles, Deborah R, Dreinhoefer, Karsten E, Betteridge, Neil, Abbasian, Mohammadreza, Abbasifard, Mitra, Abedi, Aidin, Aboye, Melka Biratu, Aravkin, Aleksandr Y, Artaman, Al, Banach, Maciej, Bensenor, Isabela M, Bhagavathula, Akshaya Srikanth, Bhat, Ajay Nagesh, Bitaraf, Saeid, Buchbinder, Rachelle, Burkart, Katrin, Chu, Dinh-Toi, Chung, Sheng-Chia, Dadras, Omid, Dai, Xiaochen ... Woolf, Anthony D (2024). Global, regional, and national burden of gout, 1990–2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. The Lancet Rheumatology, 6 (8), e507-e517. doi: 10.1016/S2665-9913(24)00117-6

Global, regional, and national burden of gout, 1990–2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021

2024

Journal Article

Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and <scp>EGFR</scp>‐mutated lung adenocarcinoma

Bhattacharjya, Abanti, Islam, Md Manowarul, Uddin, Md Ashraf, Talukder, Md Alamin, Azad, AKM, Aryal, Sunil, Paul, Bikash Kumar, Tasnim, Wahia, Almoyad, Muhammad Ali Abdulllah and Moni, Mohammad Ali (2024). Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR‐mutated lung adenocarcinoma. FEBS Open Bio, 14 (7), 1166-1191. doi: 10.1002/2211-5463.13807

Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and <scp>EGFR</scp>‐mutated lung adenocarcinoma

2024

Journal Article

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021

GBD 2021 Risk Factors Collaborators, Mamun, Abdullah A., Anderlini, Deanna, Chung, Eric, Ferrari, Alize J., Santomauro, Damian Francesco, Kanmiki, Edmund Wedam, Maravilla, Joemer C., Khan, Asaduzzaman, Moni, Mohammad Ali, McGrath, John J. and Sartorius, Benn (2024). Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet, 403 (10440), 2162-2203. doi: 10.1016/S0140-6736(24)00933-4

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021

2024

Journal Article

Burden of disease scenarios for 204 countries and territories, 2022-2050: a forecasting analysis for the Global Burden of Disease Study 2021

GBD 2021 Forecasting Collaborators, Kanmiki, Edmund Wedam, Anderlini, Deanna, Chung, Eric, Maravilla, Joemer C., Lalloo, Ratilal, McGrath, John J., Moni, Mohammad Ali and Sartorius, Benn (2024). Burden of disease scenarios for 204 countries and territories, 2022-2050: a forecasting analysis for the Global Burden of Disease Study 2021. The Lancet, 403 (10440), 2204-2256. doi: 10.1016/S0140-6736(24)00685-8

Burden of disease scenarios for 204 countries and territories, 2022-2050: a forecasting analysis for the Global Burden of Disease Study 2021

2024

Journal Article

Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

Nurjahan, , Mahbub-Or-Rashid, Md., Satu, Md. Shahriare, Tammim, Sanjana Ruhani, Sunny, Farhana Akter and Moni, Mohammad Ali (2024). Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review. Iran Journal of Computer Science, 1-23. doi: 10.1007/s42044-024-00190-z

Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

2024

Journal Article

The relationship between hair cortisol concentration and autism diagnosis

Lin, Ping-I, John, James Rufus, Masi, Anne, Ong, Lin Kooi, Mathew, Nisha E., Moni, Mohammed Ali, Eapen, Valsamma and Walker, Adam K. (2024). The relationship between hair cortisol concentration and autism diagnosis. Journal of Psychiatric Research, 176, 68-76. doi: 10.1016/j.jpsychires.2024.05.052

The relationship between hair cortisol concentration and autism diagnosis

2024

Journal Article

An effective screening of COVID‐19 pneumonia by employing chest X‐ray segmentation and attention‐based ensembled classification

Sayeed, Abu, Khansur, Nasif Osman, Srizon, Azmain Yakin, Faruk, Md. Farukuzzaman, Alyami, Salem A., Azad, AKM and Moni, Mohammad Ali (2024). An effective screening of COVID‐19 pneumonia by employing chest X‐ray segmentation and attention‐based ensembled classification. IET Image Processing, 18 (9), 2400-2416. doi: 10.1049/ipr2.13106

An effective screening of COVID‐19 pneumonia by employing chest X‐ray segmentation and attention‐based ensembled classification

2024

Journal Article

Single-cell RNA-seq data analysis reveals functionally relevant biomarkers of early brain development and their regulatory footprints in human embryonic stem cells (hESCs)

Alamin, Md, Humaira Sultana, Most, Babarinde, Isaac Adeyemi, Azad, A K M, Moni, Mohammad Ali and Xu, Haiming (2024). Single-cell RNA-seq data analysis reveals functionally relevant biomarkers of early brain development and their regulatory footprints in human embryonic stem cells (hESCs). Briefings in Bioinformatics, 25 (3) bbae230. doi: 10.1093/bib/bbae230

Single-cell RNA-seq data analysis reveals functionally relevant biomarkers of early brain development and their regulatory footprints in human embryonic stem cells (hESCs)

2024

Journal Article

Global fertility in 204 countries and territories, 1950-2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

GBD 2021 Fertility and Forecasting Collaborators, Anderlini, D., Begum, T., Chung, E., Huda, M., Kanmiki, E., Maravilla, J. C., Khan, A., Moni, M., Lalloo, R., McGrath, J. J. and Mamun, Abdullah (2024). Global fertility in 204 countries and territories, 1950-2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. The Lancet, 403 (10440), 2057-2099. doi: 10.1016/S0140-6736(24)00550-6

Global fertility in 204 countries and territories, 1950-2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

2024

Journal Article

Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor

Akter, Atika, Nosheen, Nazeela, Ahmed, Sabbir, Hossain, Mariom, Yousuf, Mohammad Abu, Almoyad, Mohammad Ali Abdullah, Hasan, Khondokar Fida and Moni, Mohammad Ali (2024). Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor. Expert Systems with Applications, 238 122347, 1-22. doi: 10.1016/j.eswa.2023.122347

Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor

2024

Journal Article

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

GBD 2021 Demographics Collaborators, Begum, Tahmina, Kanmiki, E., Maravilla, J. C., Khan, A., Moni, M., Lalloo, R., McGrath, J. J., Veerman, L. J. and Mamun, A. A. (2024). Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. The Lancet, 403 (10440), 1989-2056. doi: 10.1016/S0140-6736(24)00476-8

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

2024

Journal Article

"Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"

Islam, Md. Shofiqul, Kabir, Muhammad Nomani, Ghani, Ngahzaifa Ab, Zamli, Kamal Zuhairi, Zulkifli, Nor Saradatul Akmar, Rahman, Md. Mustafizur and Moni, Mohammad Ali (2024). "Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach". Artificial Intelligence Review, 57 (3) 62. doi: 10.1007/s10462-023-10651-9

"Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"

2024

Journal Article

Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction

Talukder, Md. Alamin, Islam, Md. Manowarul, Uddin, Md Ashraf, Hasan, Khondokar Fida, Sharmin, Selina, Alyami, Salem A. and Moni, Mohammad Ali (2024). Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction. Journal of Big Data, 11 (1) 33. doi: 10.1186/s40537-024-00886-w

Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction

2024

Journal Article

ASDNet: a robust involution‐based architecture for diagnosis of autism spectrum disorder utilising eye‐tracking technology

Mumenin, Nasirul, Yousuf, Mohammad Abu, Nashiry, Md Asif, Azad, A. K. M., Alyami, Salem A., Lio', Pietro and Moni, Mohammad Ali (2024). ASDNet: a robust involution‐based architecture for diagnosis of autism spectrum disorder utilising eye‐tracking technology. IET Computer Vision, 18 (5), 666-681. doi: 10.1049/cvi2.12271

ASDNet: a robust involution‐based architecture for diagnosis of autism spectrum disorder utilising eye‐tracking technology

2024

Journal Article

Clinically adaptable machine learning model to identify early appreciable features of diabetes In Bangladesh

Nipa, Nurjahan, Riyad, Md. Mahmudul Hasan, Satu, Md. Shahriare, Walliullah, Md., Howlader, Koushik Chandra and Moni, Mohammad Ali (2024). Clinically adaptable machine learning model to identify early appreciable features of diabetes In Bangladesh. Intelligent Medicine, 4 (1), 22-32. doi: 10.1016/j.imed.2023.01.003

Clinically adaptable machine learning model to identify early appreciable features of diabetes In Bangladesh

2024

Journal Article

Road networks and socio-demographic factors to explore COVID-19 infection during its different waves

Uddin, Shahadat, Khan, Arif, Lu, Haohui, Zhou, Fangyu, Karim, Shakir, Hajati, Farshid and Moni, Mohammad Ali (2024). Road networks and socio-demographic factors to explore COVID-19 infection during its different waves. Scientific Reports, 14 (1) 1551, 1-10. doi: 10.1038/s41598-024-51610-w

Road networks and socio-demographic factors to explore COVID-19 infection during its different waves

2024

Journal Article

Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

Pesudovs, Konrad, Lansingh, Van Charles, Kempen, John H., Tapply, Ian, Fernandes, Arthur G., Cicinelli, Maria V., Arrigo, Alessandro, Leveziel, Nicolas, Briant, Paul Svitil, Vos, Theo, Resnikoff, Serge, Taylor, Hugh R., Sedighi, Tabassom, Flaxman, Seth, Steinmetz, Jaimie, Bourne, Rupert, Pesudovs, Konrad, Lansingh, Van Charles, Kempen, John H., Tapply, Ian, Fernandes, Arthur G., Cicinelli, Maria Vittoria, Arrigo, Alessandro, Leveziel, Nicolas, Resnikoff, Serge, Taylor, Hugh R., Sedighi, Tabassom, Bikbov, Mukkharram M., Braithwaite, Tasanee ... Bourne, Rupert (2024). Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020. Eye (Basingstoke), 38 (11), 2156-2172. doi: 10.1038/s41433-024-02961-1

Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

Availability

Dr Mohammad Ali Moni is:
Available for supervision

Before you email them, read our advice on how to contact a supervisor.

Available projects

  • 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.

  • Deep Leaning Model to identify Neuroimaging biomarkers

  • Deep Learning models to solve inverse problems utiling MRI/fMRI image

  • AI-based based model development for Magnetic Resonance Imaging

  • AI-based Model development for ECG/EEG study

Supervision history

Current supervision

  • Master Philosophy

    Advancing Maternal-Fetal Health in Underserved Communities: A Computer Vision Approach

    Principal Advisor

  • Doctor Philosophy

    Developing AI-based Discission Support System utilising multimodal data

    Principal Advisor

    Other advisors: Associate Professor Asaduzzaman Khan

  • Doctor Philosophy

    Wearable devices and AI Models for Monitoring, Predicting and Assessment Post-stroke Recovery

    Principal Advisor

  • Doctor Philosophy

    Robust and Explainable AI to Solve Clinical Problems

    Principal Advisor

    Other advisors: Associate Professor Asaduzzaman Khan

  • Doctor Philosophy

    Understanding the pathophysiology of stroke using bioinformatics and statistical genetics approaches

    Principal Advisor

    Other advisors: Associate Professor Asaduzzaman Khan, Dr Jian Zeng

  • 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

  • Doctor Philosophy

    Leisure Activities and Mental Health of Australian Adolescents

    Associate Advisor

    Other advisors: Associate Professor Asaduzzaman Khan

  • Doctor Philosophy

    Service extension and cost minimization in healthcare management of peripheral healthcare organizations in Bangladesh: analysis for service improvement

    Associate Advisor

    Other advisors: Associate Professor Asaduzzaman Khan

Enquiries

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