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

367 works between 2012 and 2025
All (367) Journal Article (317) Book Chapter (4) Conference Publication (44) Book (1) Other Outputs (1)

221 - 240 of 367 works

2021

Journal Article

Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990-2019, for 204 countries and territories: the Global Burden of Diseases Study 2019

GBD 2019 HIV Collaborators, Sinshaw, Aklilu and Moni, Mohammad Ali (2021). Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990-2019, for 204 countries and territories: the Global Burden of Diseases Study 2019. The Lancet HIV, 8 (10), e633-e651. doi: 10.1016/S2352-3018(21)00152-1

Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990-2019, for 204 countries and territories: the Global Burden of Diseases Study 2019

2021

Journal Article

Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

Islam, Md Nahidul, Sulaiman, Norizam, Farid, Fahmid Al, Uddin, Jia, Alyami, Salem A., Rashid, Mamunur, Majeed, Anwar P. P. Abdul and Moni, Mohammad Ali (2021). Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline. PeerJ Computer Science, 7 e638, 1-28. doi: 10.7717/peerj-cs.638

Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

2021

Journal Article

Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

GBD 2019 Under-5 Mortality Collaborators, Mamun, Abdullah A., Lalloo, Ratilal, Anderlini, Deanna, McGrath, John, Uddin, Riaz and Moni, Mohammad Ali (2021). Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019. The Lancet, 398 (10303), 870-905. doi: 10.1016/S0140-6736(21)01207-1

Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

2021

Journal Article

Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19

Auwul, Md Rabiul, Rahman, Md Rezanur, Gov, Esra, Shahjaman, Md and Moni, Mohammad Ali (2021). Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19. Briefings in Bioinformatics, 22 (5) bbab120. doi: 10.1093/bib/bbab120

Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19

2021

Journal Article

Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison

Ali, Md Mamun, Paul, Bikash Kumar, Ahmed, Kawsar, Bui, Francis M., Quinn, Julian M.W. and Moni, Mohammad Ali (2021). Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison. Computers in Biology and Medicine, 136 104672, 104672. doi: 10.1016/j.compbiomed.2021.104672

Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison

2021

Journal Article

Recognition of human emotions using EEG signals: A review

Rahman, Md. Mustafizur, Sarkar, Ajay Krishno, Hossain, Md. Amzad, Hossain, Md. Selim, Islam, Md. Rabiul, Hossain, Md. Biplob, Quinn, Julian M.W. and Moni, Mohammad Ali (2021). Recognition of human emotions using EEG signals: A review. Computers in Biology and Medicine, 136 104696, 104696. doi: 10.1016/j.compbiomed.2021.104696

Recognition of human emotions using EEG signals: A review

2021

Conference Publication

Lung cancer prediction using curriculum learning based deep neural networks

Zhou, Jackson, Khushi, Matloob, Moni, Mohammad Ali, Uddin, Shahadat and Poon, Simon K. (2021). Lung cancer prediction using curriculum learning based deep neural networks. 2021 IEEE International Conference on Digital Health (ICDH), Chicago, IL USA, 5-10 September 2021. Piscataway, NJ USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICDH52753.2021.00013

Lung cancer prediction using curriculum learning based deep neural networks

2021

Journal Article

Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression

Rahman, Md Habibur, Rana, Humayan Kabir, Peng, Silong, Hu, Xiyuan, Chen, Chen, Quinn, Julian M. W and Moni, Mohammad Ali (2021). Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression. Briefings in Bioinformatics, 22 (5) bbaa365. doi: 10.1093/bib/bbaa365

Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression

2021

Journal Article

EEG channel correlation based model for emotion recognition

Islam, Md. Rabiul, Islam, Md. Milon, Rahman, Md. Mustafizur, Mondal, Chayan, Singha, Suvojit Kumar, Ahmad, Mohiuddin, Awal, Abdul, Islam, Md. Saiful and Moni, Mohammad Ali (2021). EEG channel correlation based model for emotion recognition. Computers in Biology and Medicine, 136 104757, 104757. doi: 10.1016/j.compbiomed.2021.104757

EEG channel correlation based model for emotion recognition

2021

Journal Article

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients

Mahmud, S. M. Hasan, Al-Mustanjid, Md, Akter, Farzana, Rahman, Md Shazzadur, Ahmed, Kawsar, Rahman, Md Habibur, Chen, Wenyu and Moni, Mohammad Ali (2021). Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients. Briefings in Bioinformatics, 22 (5) bbab115. doi: 10.1093/bib/bbab115

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients

2021

Journal Article

Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes

Islam, M. Babul, Chowdhury, Utpala Nanda, Nain, Zulkar, Uddin, Shahadat, Ahmed, Mohammad Boshir and Moni, Mohammad Ali (2021). Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes. Computers in Biology and Medicine, 136 104668, 104668. doi: 10.1016/j.compbiomed.2021.104668

Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes

2021

Journal Article

Covid-19 pandemic outbreak in the subcontinent: A data driven analysis

Singh, Bikash Chandra, Alom, Zulfikar, Hu, Haibo, Rahman, Mohammad Muntasir, Baowaly, Mrinal Kanti, Aung, Zeyar, Azim, Mohammad Abdul and Moni, Mohammad Ali (2021). Covid-19 pandemic outbreak in the subcontinent: A data driven analysis. Journal of Personalized Medicine, 11 (9) 889, 889. doi: 10.3390/jpm11090889

Covid-19 pandemic outbreak in the subcontinent: A data driven analysis

2021

Journal Article

PreDTIs: Prediction of drug-Target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques

Mahmud, S. M. Hasan, Chen, Wenyu, Liu, Yongsheng, Awal, Md Abdul, Ahmed, Kawsar, Rahman, Md Habibur and Moni, Mohammad Ali (2021). PreDTIs: Prediction of drug-Target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques. Briefings in Bioinformatics, 22 (5) bbab046. doi: 10.1093/bib/bbab046

PreDTIs: Prediction of drug-Target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques

2021

Journal Article

COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing

Hasan, Md. Kamrul, Jawad, Md. Tasnim, Hasan, Kazi Nasim Imtiaz, Partha, Sajal Basak, Masba, Md. Masum Al, Saha, Shumit and Moni, Mohammad Ali (2021). COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing. Informatics in Medicine Unlocked, 26 100709, 100709. doi: 10.1016/j.imu.2021.100709

COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing

2021

Journal Article

Ibitter‐fuse: a novel sequence‐based bitter peptide predictor by fusing multi‐view features

Charoenkwan, Phasit, Nantasenamat, Chanin, Hasan, Md. Mehedi, Moni, Mohammad Ali, Lio, Pietro and Shoombuatong, Watshara (2021). Ibitter‐fuse: a novel sequence‐based bitter peptide predictor by fusing multi‐view features. International Journal of Molecular Sciences, 22 (16) 8958, 8958. doi: 10.3390/ijms22168958

Ibitter‐fuse: a novel sequence‐based bitter peptide predictor by fusing multi‐view features

2021

Journal Article

TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets

Satu, Md. Shahriare, Khan, Md. Imran, Mahmud, Mufti, Uddin, Shahadat, Summers, Matthew A., Quinn, Julian M.W. and Moni, Mohammad Ali (2021). TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets. Knowledge-Based Systems, 226 107126, 107126. doi: 10.1016/j.knosys.2021.107126

TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets

2021

Journal Article

Use of multidimensional item response theory methods for dementia prevalence prediction: an example using the Health and Retirement Survey and the Aging, Demographics, and Memory Study

Nichols, Emma, Abd-Allah, Foad, Abdoli, Amir, Abualhasan, Ahmed, Abu-Gharbieh, Eman, Afshin, Ashkan, Akinyemi, Rufus Olusola, Alanezi, Fahad Mashhour, Alipour, Vahid, Almasi-Hashiani, Amir, Arabloo, Jalal, Ashraf-Ganjouei, Amir, Ayano, Getinet, Ayuso-Mateos, Jose L., Baig, Atif Amin, Banach, Maciej, Barboza, Miguel A., Barker-Collo, Suzanne Lyn, Baune, Bernhard T., Bhagavathula, Akshaya Srikanth, Bhattacharyya, Krittika, Bijani, Ali, Biswas, Atanu, Boloor, Archith, Brayne, Carol, Brenner, Hermann, Burkart, Katrin, Burugina Nagaraja, Sharath, Carvalho, Felix ... Vos, Theo (2021). Use of multidimensional item response theory methods for dementia prevalence prediction: an example using the Health and Retirement Survey and the Aging, Demographics, and Memory Study. BMC Medical Informatics and Decision Making, 21 (1) 241. doi: 10.1186/s12911-021-01590-y

Use of multidimensional item response theory methods for dementia prevalence prediction: an example using the Health and Retirement Survey and the Aging, Demographics, and Memory Study

2021

Journal Article

Measuring routine childhood vaccination coverage in 204 countries and territories, 1980–2019: a systematic analysis for the Global Burden of Disease Study 2020, Release 1

Galles, Natalie C, Liu, Patrick Y, Updike, Rachel L, Fullman, Nancy, Nguyen, Jason, Rolfe, Sam, Sbarra, Alyssa N, Schipp, Megan F, Marks, Ashley, Abady, Gdiom Gebreheat, Abbas, Kaja M, Abbasi, Sumra Wajid, Abbastabar, Hedayat, Abd-Allah, Foad, Abdoli, Amir, Abolhassani, Hassan, Abosetugn, Akine Eshete, Adabi, Maryam, Adamu, Abdu A, Adetokunboh, Olatunji O, Adnani, Qorinah Estiningtyas Sakilah, Advani, Shailesh M, Afzal, Saira, Aghamir, Seyed Mohammad Kazem, Ahinkorah, Bright Opoku, Ahmad, Sohail, Ahmad, Tauseef, Ahmadi, Sepideh, Ahmed, Haroon ... Yuce, D. (2021). Measuring routine childhood vaccination coverage in 204 countries and territories, 1980–2019: a systematic analysis for the Global Burden of Disease Study 2020, Release 1. The Lancet, 398 (10299), 503-521. doi: 10.1016/S0140-6736(21)00984-3

Measuring routine childhood vaccination coverage in 204 countries and territories, 1980–2019: a systematic analysis for the Global Burden of Disease Study 2020, Release 1

2021

Journal Article

Machine learning approaches to identify patient comorbidities and symptoms that increased risk of mortality in covid-19

Aktar, Sakifa, Talukder, Ashis, Ahamad, Md. Martuza, Kamal, A. H.M., Khan, Jahidur Rahman, Protikuzzaman, Md., Hossain, Nasif, Azad, A. K.M., Quinn, Julian M. W., Summers, Mathew A., Liaw, Teng, Eapen, Valsamma and Moni, Mohammad Ali (2021). Machine learning approaches to identify patient comorbidities and symptoms that increased risk of mortality in covid-19. Diagnostics, 11 (8) 1383, 1383. doi: 10.3390/diagnostics11081383

Machine learning approaches to identify patient comorbidities and symptoms that increased risk of mortality in covid-19

2021

Journal Article

Promising anticancer activity of [bis(1,8‐quinolato)palladium (ii)] alone and in combination

Alam, Md Nur, Moni, Mohammad Ali, Yu, Jun Q., Beale, Philip, Turner, Peter, Proschogo, Nick, Rahman, Mohammad Azizur, Hossain, M. Pear and Huq, Fazlul (2021). Promising anticancer activity of [bis(1,8‐quinolato)palladium (ii)] alone and in combination. International Journal of Molecular Sciences, 22 (16) 8471, 8471. doi: 10.3390/ijms22168471

Promising anticancer activity of [bis(1,8‐quinolato)palladium (ii)] alone and in combination

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

    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

    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

    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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