Dr Mohammad Ali Moni
Dr

Mohammad Ali Moni

Email: 

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

323 works between 2012 and 2024
All (323) Journal Article (282) Book Chapter (4) Conference Publication (36) Book (1)

21 - 40 of 323 works

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

2024

Journal Article

Advances in Artificial Intelligence and Blockchain Technologies for Early Detection of Human Diseases

Shammi, Shumaiya Akter, Ghosh, Pronab, Sutradhar, Ananda, Shamrat, F M Javed Mehedi, Moni, Mohammad Ali and Oliveira, Thiago Eustaquio Alves de (2024). Advances in Artificial Intelligence and Blockchain Technologies for Early Detection of Human Diseases. IEEE Transactions on Computational Social Systems, 1-28. doi: 10.1109/tcss.2024.3449748

Advances in Artificial Intelligence and Blockchain Technologies for Early Detection of Human Diseases

2024

Journal Article

A Robust Deep-Learning Model to Detect Major Depressive Disorder Utilising EEG Signals

Anik, Israq Ahmed, Kamal, A H M, Kabir, Muhammad Ashad, Uddin, Shahadat and Moni, Mohammad Ali (2024). A Robust Deep-Learning Model to Detect Major Depressive Disorder Utilising EEG Signals. IEEE Transactions on Artificial Intelligence, PP (99), 1-10. doi: 10.1109/tai.2024.3394792

A Robust Deep-Learning Model to Detect Major Depressive Disorder Utilising EEG Signals

2024

Journal Article

An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection

Mehedi Shamrat, F M Javed, Shakil, Rashiduzzaman, Sharmin, , Hoque ovy, Nazmul, Akter, Bonna, Ahmed, Md Zunayed, Ahmed, Kawsar, Bui, Francis M. and Moni, Mohammad Ali (2024). An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection. Healthcare Analytics, 5 100303, 100303. doi: 10.1016/j.health.2024.100303

An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection

2024

Journal Article

A comparative evaluation of machine learning ensemble approaches for disease prediction using multiple datasets

Mahajan, Palak, Uddin, Shahadat, Hajati, Farshid, Moni, Mohammad Ali and Gide, Ergun (2024). A comparative evaluation of machine learning ensemble approaches for disease prediction using multiple datasets. Health and Technology, 14 (3), 597-613. doi: 10.1007/s12553-024-00835-w

A comparative evaluation of machine learning ensemble approaches for disease prediction using multiple datasets

2024

Journal Article

Automated Detection of Harmful Insects in Agriculture: A Smart Framework Leveraging IoT, Machine Learning, and Blockchain

Rahman, Wahidur, Hossain, Muhammad Minoar, Hasan, Md. Mahedi, Iqbal, Md. Sadiq, Rahman, Mohammad Motiur, Fida Hasan, Khondokar and Moni, Mohammad Ali (2024). Automated Detection of Harmful Insects in Agriculture: A Smart Framework Leveraging IoT, Machine Learning, and Blockchain. IEEE Transactions on Artificial Intelligence, PP (99), 1-12. doi: 10.1109/tai.2024.3394799

Automated Detection of Harmful Insects in Agriculture: A Smart Framework Leveraging IoT, Machine Learning, and Blockchain

2024

Journal Article

Toward reliable diabetes prediction: Innovations in data engineering and machine learning applications

Talukder, Md. Alamin, Islam, Md. Manowarul, Uddin, Md Ashraf, Kazi, Mohsin, Khalid, Majdi, Akhter, Arnisha and Moni, Mohammad Ali (2024). Toward reliable diabetes prediction: Innovations in data engineering and machine learning applications. Digital Health, 10. doi: 10.1177/20552076241271867

Toward reliable diabetes prediction: Innovations in data engineering and machine learning applications

2024

Journal Article

Review of physical layer security in molecular internet of nano-things

Qiu, Song, Wei, Zhuangkun, Huang, Yu, Abbaszadeh, Mahmoud, Charmet, Jerome, Li, Bin and Guo, Weisi (2024). Review of physical layer security in molecular internet of nano-things. IEEE Transactions on NanoBioscience, 23 (1), 91-100. doi: 10.1109/tnb.2023.3285973

Review of physical layer security in molecular internet of nano-things

2024

Journal Article

Deep and Shallow Learning Model-based Sleep Apnea Diagnosis Systems: A Comprehensive Study

Raisa, Roksana Akter, Rodela, Ayesha Siddika, Yousuf, Mohammad Abu, Azad, AKM, Alyami, Salem A., Liò, Pietro, Islam, Md Zahidul, Pogrebna, Ganna and Moni, Mohammad Ali (2024). Deep and Shallow Learning Model-based Sleep Apnea Diagnosis Systems: A Comprehensive Study. IEEE Access, 12, 1-1. doi: 10.1109/access.2024.3426928

Deep and Shallow Learning Model-based Sleep Apnea Diagnosis Systems: A Comprehensive Study

2024

Journal Article

Correction to: Global estimates on the number of people blind or visually impaired by cataract_ a meta-analysis from 2000 to 2020 (Eye, (2024), 10.1038/s41433-024-02961-1)

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, Flaxman, Seth, Bikbov, Mukkharram M., Braithwaite, Tasanee, Bron, Alain, Cheng, Ching-Yu, Del Monte, Monte A., Ehrlich, Joshua R., Ellwein, Leon B., Friedman, David, Furtado, João M., Gazzard, Gus, George, Ronnie, Hartnett, M. Elizabeth, Jonas, Jost B., Kahloun, Rim, Khairallah, Moncef, Khanna, Rohit C., Leasher, Janet ... Steinmetz, Jaimie D. (2024). Correction to: Global estimates on the number of people blind or visually impaired by cataract_ a meta-analysis from 2000 to 2020 (Eye, (2024), 10.1038/s41433-024-02961-1). Eye (Basingstoke), 38 (11), 2229-2231. doi: 10.1038/s41433-024-03161-7

Correction to: Global estimates on the number of people blind or visually impaired by cataract_ a meta-analysis from 2000 to 2020 (Eye, (2024), 10.1038/s41433-024-02961-1)

2023

Journal Article

BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients

Sutradhar, Ananda, Al Rafi, Mustahsin, Shamrat, F M Javed Mehedi, Ghosh, Pronab, Das, Subrata, Islam, Md Anaytul, Ahmed, Kawsar, Zhou, Xujuan, Azad, A. K.M., Alyami, Salem A. and Moni, Mohammad Ali (2023). BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients. Scientific Reports, 13 (1) 22874, 1-16. doi: 10.1038/s41598-023-48486-7

BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients

2023

Journal Article

Network based approach to identify interactions between Type 2 diabetes and cancer comorbidities

Nayan, Saidul Islam, Rahman, Md Habibur, Hasan, Md. Mehedi, Raj, Sheikh Md. Razibul Hasan, Almoyad, Mohammad Ali Abdullah, Liò, Pietro and Moni, Mohammad Ali (2023). Network based approach to identify interactions between Type 2 diabetes and cancer comorbidities. Life Sciences, 335 122244, 1-16. doi: 10.1016/j.lfs.2023.122244

Network based approach to identify interactions between Type 2 diabetes and cancer comorbidities

2023

Conference Publication

Observation of heart attack patients utilizing machine learning with monarch butterfly optimization and IoT

Rahman, Wahidur, Abul Ala Walid, Md., Saklain Galib, S. M., Rokhsana, Kaniz, Abdul Hai, Talha Bin, Mohammad Azad, Mir and Ali Moni, Mohammad (2023). Observation of heart attack patients utilizing machine learning with monarch butterfly optimization and IoT. 2023 26th International Conference on Computer and Information Technology, ICCIT 2023, Cox's Bazar, Bangladesh, 13-15 December 2023. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/iccit60459.2023.10441444

Observation of heart attack patients utilizing machine learning with monarch butterfly optimization and IoT

2023

Journal Article

Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model

Asadi, Shirin, Tartibian, Bakhtyar and Moni, Mohammad Ali (2023). Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model. Scientific Reports, 13 (1) 8207, 1-10. doi: 10.1038/s41598-023-34974-3

Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model

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

  • 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

    Robust and Explainable AI to Solve Clinical Problems

    Principal Advisor

    Other advisors: Associate Professor Asaduzzaman Khan

  • Master Philosophy

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

    Principal Advisor

  • 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

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

    Principal Advisor

  • Doctor Philosophy

    Developing AI-based Discission Support System utilising multimodal data

    Principal 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

For media enquiries about Dr Mohammad Ali Moni's areas of expertise, story ideas and help finding experts, contact our Media team:

communications@uq.edu.au