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

365 works between 2012 and 2025
All (365) Journal Article (316) Book Chapter (4) Conference Publication (43) Book (1) Other Outputs (1)

61 - 80 of 365 works

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

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

Conference Publication

Bengali Cyberbullying: Detection, Categorization, and Gender Bias Analysis

Hossain, Md. Mithun, Hossain, Md. Shakil, Chaki, Sudipto, Rahman, Md. Saifur and Moni, Mohammad Ali (2024). Bengali Cyberbullying: Detection, Categorization, and Gender Bias Analysis. IEEE Computer Society. doi: 10.1109/ICDMW65004.2024.00027

Bengali Cyberbullying: Detection, Categorization, and Gender Bias Analysis

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 122959, 1-29. doi: 10.1109/access.2024.3426928

Deep and shallow learning model-based sleep apnea diagnosis systems: a comprehensive study

2024

Journal Article

Corrigendum to “DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model” [Results in Engineering 24 (2024) 102878] (Results in Engineering (2024) 24, (S2590123024011332), (10.1016/j.rineng.2024.102878))

Rahman, Md. Ashikur, Ali, Md. Mamun, Ahmed, Kawsar, Mahmud, Imran, Bui, Francis M., Chen, Li, Kumar, Santosh and Moni, Mohammad Ali (2024). Corrigendum to “DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model” [Results in Engineering 24 (2024) 102878] (Results in Engineering (2024) 24, (S2590123024011332), (10.1016/j.rineng.2024.102878)). Results in Engineering, 24 103178, 103178. doi: 10.1016/j.rineng.2024.103178

Corrigendum to “DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model” [Results in Engineering 24 (2024) 102878] (Results in Engineering (2024) 24, (S2590123024011332), (10.1016/j.rineng.2024.102878))

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)

2024

Journal Article

Global, regional, and national burden of pulmonary arterial hypertension, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Leary, Peter J, Lindstrom, Megan, Johnson, Catherine O, Emmons-Bell, Sophia, Rich, Stuart, Corris, Paul A, DuBrock, Hilary M, Ventetuolo, Corey E, Abate, Yohannes Habtegiorgis, Abdelmasseh, Michael, Aboagye, Richard Gyan, Abualruz, Hasan, Abu-Gharbieh, Eman, Aburuz, Salahdein, Adamu, Lawan Hassan, Adão, Rui, Addo, Isaac Yeboah, Adedoyin, Rufus Adesoji, Adetunji, Juliana Bunmi, Adzigbli, Leticia Akua, Ahinkorah, Bright Opoku, Ahmad, Firdos, Ahmadzade, Amir Mahmoud, Ahmed, Ayman, Ahmed, Haroon, Ahmed, Syed Anees, Akhlaghi, Shiva, Akkaif, Mohammed Ahmed, Al Awaidy, Salah ... Roth, Gregory A (2024). Global, regional, and national burden of pulmonary arterial hypertension, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet Respiratory Medicine, 13 (1), 69-79. doi: 10.1016/S2213-2600(24)00295-9

Global, regional, and national burden of pulmonary arterial hypertension, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

2024

Conference Publication

GDRNet: A Novel Graph Neural Network Architecture for Diabetic Retinopathy Detection

Hossain, Shahed, Hasan, Md. Zahid, Jim, Risul Islam, Shuva, Taslima Ferdaus, Rahman, Md. Tanvir, Bulbul, Abdullah Al-Mamun, Khan, Risala Tasin, Kaiser, M. Shamim and Moni, Mohammad Ali (2024). GDRNet: A Novel Graph Neural Network Architecture for Diabetic Retinopathy Detection. IEEE Computer Society. doi: 10.1109/ICDMW65004.2024.00055

GDRNet: A Novel Graph Neural Network Architecture for Diabetic Retinopathy Detection

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

The burden of diseases and risk factors in Bangladesh, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Islam, Sheikh Mohammed Shariful, Uddin, Riaz, Das, Subasish, Ahmed, Syed Imran, Zaman, Sojib Bin, Alif, Sheikh Mohammad, Hossen, Md Tanvir, Sarker, Malabika, Siopis, George, Livingstone, Katherine M., Mehlman, Max L., Rahman, Md. Marufur, Chowdhury, Rahat I., Alim, Md. Abdul, Choudhury, Sohel Reza, Ahmed, Syed Masud, Adhikary, Ripon Kumar, Anjum, Afifa, Banik, Palash Chandra, Chowdhury, Fazle Rabbi, Faruk, Md Omar, Gupta, Rajat Das, Hannan, Md Abdul, Haque, Md Nuruzzaman, Haque, Syed Emdadul, Hasan, M. Tasdik, Hossain, Md Belal, Hossain, Md Mahbub, Hossain, Muttaquina ... Naghavi, Mohsen (2023). The burden of diseases and risk factors in Bangladesh, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet Global Health, 11 (12), e1931-e1942. doi: 10.1016/S2214-109X(23)00432-1

The burden of diseases and risk factors in Bangladesh, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

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

2023

Journal Article

A computational approach to design a polyvalent vaccine against human respiratory syncytial virus

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

A computational approach to design a polyvalent vaccine against human respiratory syncytial virus

2023

Conference Publication

CervixMed: Detecting cervical cancer based on combinational data using hybrid architecture

Gupta, Debashis, Golder, Aditi, Haque, Md. Mahfuzul and Moni, Mohammad Ali (2023). CervixMed: Detecting cervical cancer based on combinational data using hybrid architecture. 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Port Macquarie, NSW Australia, 28 November - 1 December 2023. Piscataway, NJ United States: IEEE. doi: 10.1109/dicta60407.2023.00085

CervixMed: Detecting cervical cancer based on combinational data using hybrid architecture

2023

Journal Article

An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning

Talukder, Md. Alamin, Islam, Md. Manowarul, Uddin, Md. Ashraf, Akhter, Arnisha, Pramanik, Md. Alamgir Jalil, Aryal, Sunil, Almoyad, Muhammad Ali Abdulllah, Hasan, Khondokar Fida and Moni, Mohammad Ali (2023). An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning. Expert Systems with Applications, 230 120534, 120534. doi: 10.1016/j.eswa.2023.120534

An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning

2023

Journal Article

Identification of genetic biomarkers, drug targets and agents for respiratory diseases utilising integrated bioinformatics approaches

Ahmed, Fee Faysal, Das, Arnob Dip, Sumi, Mst. Joynab, Islam, Md. Zohurul, Rahman, Md. Shahedur, Rashid, Md. Harun, Alyami, Salem A., Alotaibi, Naif, Azad, A. K.M. and Moni, Mohammad Ali (2023). Identification of genetic biomarkers, drug targets and agents for respiratory diseases utilising integrated bioinformatics approaches. Scientific Reports, 13 (1) 19072, 1-14. doi: 10.1038/s41598-023-46455-8

Identification of genetic biomarkers, drug targets and agents for respiratory diseases utilising integrated bioinformatics approaches

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

    Understanding the pathophysiology of stroke using bioinformatics and statistical genetics

    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

    Understanding the pathophysiology of stroke using bioinformatics and statistical genetics

    Principal Advisor

    Other advisors: Associate Professor Asaduzzaman Khan, Dr Jian Zeng

  • 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

    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

Enquiries

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