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Dr Mohammad Ali Moni
Dr

Mohammad Ali Moni

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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

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

Works

Search Professor Mohammad Ali Moni’s works on UQ eSpace

416 works between 2012 and 2026

41 - 60 of 416 works

2025

Journal Article

Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

Steel, Nicholas, Bauer-Staeb, Clarissa Maria Mercedes, Ford, John A, Abbafati, Cristiana, Abdalla, Mohammed Altigani, Abdelkader, Atef, Abdi, Parsa, Abeldaño Zuñiga, Roberto Ariel, Abiodun, Olugbenga Olusola, Abolhassani, Hassan, Abu-Gharbieh, Eman, Abukhadijah, Hana J, Abu-Zaid, Ahmed, Addo, Isaac Yeboah, Addolorato, Giovanni, Adekanmbi, Victor, Adetunji, Juliana Bunmi, Adeyeoluwa, Temitayo Esther, Agardh, Emilie E, Agyemang-Duah, Williams, Ahmad, Danish, Ahmed, Anisuddin, Ahmed, Ayman, Ahmed, Syed Anees, Akinosoglou, Karolina, Akkaif, Mohammed Ahmed, Al Awaidy, Salah, Al Hasan, Syed Mahfuz, Al Zaabi, Omar Ali Mohammed ... Newton, John N (2025). Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021. The Lancet Public Health, 10 (3), e172-e188. doi: 10.1016/S2468-2667(25)00009-X

Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

2025

Journal Article

A privacy-preserving dependable deep federated learning model for identifying new infections from genome sequences

Mehedi, Sk. Tanzir, Abdulrazak, Lway Faisal, Ahmed, Kawsar, Uddin, Muhammad Shahin, Bui, Francis M., Chen, Li, Moni, Mohammad Ali and Al-Zahrani, Fahad Ahmed (2025). A privacy-preserving dependable deep federated learning model for identifying new infections from genome sequences. Scientific Reports, 15 (1) 7291, 1-24. doi: 10.1038/s41598-025-89612-x

A privacy-preserving dependable deep federated learning model for identifying new infections from genome sequences

2025

Journal Article

A hybrid transfer learning framework for brain tumor diagnosis

Tonni, Sadia Islam, Sheakh, Md. Alif, Tahosin, Mst. Sazia, Hasan, Md. Zahid, Shuva, Taslima Ferdaus, Bhuiyan, Touhid, Almoyad, Muhammad Ali Abdullah, Orka, Nabil Anan, Rahman, Md. Tanvir, Khan, Risala Tasin, Kaiser, M. Shamim and Moni, Mohammad Ali (2025). A hybrid transfer learning framework for brain tumor diagnosis. Advanced Intelligent Systems, 7 (3) 2400495, 1-10. doi: 10.1002/aisy.202400495

A hybrid transfer learning framework for brain tumor diagnosis

2025

Journal Article

WFFS—An ensemble feature selection algorithm for heterogeneous traffic accident data analysis

Rajee, Alimul, Satu, Md. Shahriare, Abedin, Mohammad Zoynul, Ali, K.M. Akkas, Aloteibi, Saad and Moni, Mohammad Ali (2025). WFFS—An ensemble feature selection algorithm for heterogeneous traffic accident data analysis. Knowledge-Based Systems, 311 113089, 1-15. doi: 10.1016/j.knosys.2025.113089

WFFS—An ensemble feature selection algorithm for heterogeneous traffic accident data analysis

2025

Journal Article

A multi-agent and attention-aware enhanced CNN-BiLSTM model for human activity recognition for enhanced disability assistance

Khatun, Mst Alema, Yousuf, Mohammad Abu, Turna, Taskin Noor, Azad, AKM, Alyami, Salem A. and Moni, Mohammad Ali (2025). A multi-agent and attention-aware enhanced CNN-BiLSTM model for human activity recognition for enhanced disability assistance. Diagnostics, 15 (5) 537, 537. doi: 10.3390/diagnostics15050537

A multi-agent and attention-aware enhanced CNN-BiLSTM model for human activity recognition for enhanced disability assistance

2025

Journal Article

Quantum deep learning in neuroinformatics: a systematic review

Orka, Nabil Anan, Awal, Md. Abdul, Liò, Pietro, Pogrebna, Ganna, Ross, Allen G. and Moni, Mohammad Ali (2025). Quantum deep learning in neuroinformatics: a systematic review. Artificial Intelligence Review, 58 (5) 134, 1-24. doi: 10.1007/s10462-025-11136-7

Quantum deep learning in neuroinformatics: a systematic review

2025

Journal Article

A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images

Nahiduzzaman, Md., Abdulrazak, Lway Faisal, Kibria, Hafsa Binte, Khandakar, Amith, Ayari, Mohamed Arselene, Ahamed, Md. Faysal, Ahsan, Mominul, Haider, Julfikar, Moni, Mohammad Ali and Kowalski, Marcin (2025). A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images. Scientific Reports, 15 (1) 1649, 1-21. doi: 10.1038/s41598-025-85874-7

A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images

2025

Journal Article

iMRSA-Fuse: A fast and accurate computational approach for predicting anti-MRSA peptides by fusing multi-view information

Charoenkwan, Phasit, Schaduangrat, Nalini, Moni, Mohammad Ali and Shoombuatong, Watshara (2025). iMRSA-Fuse: A fast and accurate computational approach for predicting anti-MRSA peptides by fusing multi-view information. IEEE Transactions on Computational Biology and Bioinformatics, 22 (1), 2-12. doi: 10.1109/tcbbio.2024.3496503

iMRSA-Fuse: A fast and accurate computational approach for predicting anti-MRSA peptides by fusing multi-view information

2025

Conference Publication

IoT-Enabled Health Assistance for Post-disaster Scenarios

Hossain, Soikat, Sarkar, Ratna R., Yousuf, Mohammad Abu and Moni, Mohammad Ali (2025). IoT-Enabled Health Assistance for Post-disaster Scenarios. Trends in Electronics and Health Informatics TEHI 2023, Dhaka, Bangladesh, 26–27 December 2023. Singapore: Springer. doi: 10.1007/978-981-97-3937-0_44

IoT-Enabled Health Assistance for Post-disaster Scenarios

2025

Book Chapter

A Multi-BranchCNN-LSTM Based Human Activity Recognition Using Wearable and Smartphone Sensors

Khatun, Mst. Alema, Yousuf, Mohammad Abu and Moni, Mohammad Ali (2025). A Multi-BranchCNN-LSTM Based Human Activity Recognition Using Wearable and Smartphone Sensors. Intelligent Networks and Systems: Advanced Technologies and Applications. (pp. 27-39) CRC Press. doi: 10.1201/9781032659770-3

A Multi-BranchCNN-LSTM Based Human Activity Recognition Using Wearable and Smartphone Sensors

2025

Book

Graphene in wearable sensors for health monitoring

Debnath, Sourabhi, Debnath, Tanmoy, Moni, Mohammad Ali and Paul, Manoranjan (2025). Graphene in wearable sensors for health monitoring. Singapore: Springer. doi: 10.1007/978-981-96-8850-0

Graphene in wearable sensors for health monitoring

2025

Journal Article

Global, regional, and national progress towards the 2030 global nutrition targets and forecasts to 2050: a systematic analysis for the Global Burden of Disease Study 2021

Arndt, Michael Benjamin, Abate, Yohannes Habtegiorgis, Abbasi-Kangevari, Mohsen, Abd ElHafeez, Samar, Abdelmasseh, Michael, Abd-Elsalam, Sherief, Abdulah, Deldar Morad, Abdulkader, Rizwan Suliankatchi, Abidi, Hassan, Abiodun, Olumide, Aboagye, Richard Gyan, Abolhassani, Hassan, Abtew, Yonas Derso, Abu-Gharbieh, Eman, Abu-Rmeileh, Niveen ME, Acuna, Juan Manuel, Adamu, Kidist, Adane, Denberu Eshetie, Addo, Isaac Yeboah, Adeyinka, Daniel Adedayo, Adnani, Qorinah Estiningtyas Sakilah, Afolabi, Aanuoluwapo Adeyimika, Afrashteh, Fatemeh, Afzal, Saira, Agodi, Antonella, Ahinkorah, Bright Opoku, Ahmad, Aqeel, Ahmad, Sajjad, Ahmad, Tauseef ... Reiner, Robert C (2025). Global, regional, and national progress towards the 2030 global nutrition targets and forecasts to 2050: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet, 404 (10471), 2543-2583. doi: 10.1016/S0140-6736(24)01821-X

Global, regional, and national progress towards the 2030 global nutrition targets and forecasts to 2050: a systematic analysis for the Global Burden of Disease Study 2021

2025

Book Chapter

Explainable AI-Based Heart Attack Prediction Model Using Various Machine Learning and Ensemble Learning Approaches

Palash, Md Istakiak Adnan, Rahman, Muntarin, Yousuf, Mohammad Abu and Moni, Mohammad Ali (2025). Explainable AI-Based Heart Attack Prediction Model Using Various Machine Learning and Ensemble Learning Approaches. Applied Intelligence for Healthcare Informatics: Techniques and Applications. (pp. 1-13) CRC Press. doi: 10.1201/9781003583363-1

Explainable AI-Based Heart Attack Prediction Model Using Various Machine Learning and Ensemble Learning Approaches

2025

Journal Article

Corrections to “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 (2025). Corrections to “Deep and Shallow Learning Model-Based Sleep Apnea Diagnosis Systems: A Comprehensive Study”. IEEE Access, 13, 48033-48033. doi: 10.1109/access.2025.3551391

Corrections to “Deep and Shallow Learning Model-Based Sleep Apnea Diagnosis Systems: A Comprehensive Study”

2025

Journal Article

Multimodal marvels of deep learning in medical diagnosis using image, speech, and text: a comprehensive review of COVID-19 detection

Islam, Md Shofiqul, Hasan, Khondokar Fida, Shajeeb, Hasibul Hossain, Rana, Humayan Kabir, Rahman, Md. Saifur, Hasan, Md. Munirul, Azad, AKM, Abdullah, Ibrahim and Moni, Mohammad Ali (2025). Multimodal marvels of deep learning in medical diagnosis using image, speech, and text: a comprehensive review of COVID-19 detection. AI Open, 6, 12-44. doi: 10.1016/j.aiopen.2025.01.003

Multimodal marvels of deep learning in medical diagnosis using image, speech, and text: a comprehensive review of COVID-19 detection

2025

Journal Article

Global, regional and national burden of dietary iron deficiency from 1990 to 2021: a Global Burden of Disease study

Lee, Sooji, Son, Yejun, Hwang, Jiyoung, Kim, Min Seo, McLaughlin, Susan A., Vilchis-Tella, Victor, Zoeckler, Leo, Perumal, Nandita, Zyoud, Sa’ed H., Zoghi, Ghazal, Zia, Hafsa, Zhumagaliuly, Abzal, Zhong, Claire Chenwen, Zhang, Liqun, Zhang, Haijun, Zeru, Naod Gebrekrstos, Zeariya, Mohammed G. M., Zare, Iman, Zamora, Nelson, Zaman, Burhan Abdullah, Yu, Chuanhua, Yonemoto, Naohiro, Yin, Dehui, Yigezu, Muluken, Yesuf, Subah Abderehim, Yaya, Sanni, Yarahmadi, Amir, Yadav, Vikas, Xu, Xiaoyue ... Kassebaum, Nicholas. J. (2025). Global, regional and national burden of dietary iron deficiency from 1990 to 2021: a Global Burden of Disease study. Nature Medicine, 31 (6), 1809-1829. doi: 10.1038/s41591-025-03624-8

Global, regional and national burden of dietary iron deficiency from 1990 to 2021: a Global Burden of Disease study

2025

Journal Article

Understanding Neurocognition with Deep Learning and MRI: A Systematic Review

Rahman, Md. Tanvir, Orka, Nabil Anan, Khan, Asaduzzaman, Liò, Pietro and Moni, Mohammad Ali (2025). Understanding Neurocognition with Deep Learning and MRI: A Systematic Review. IEEE Transactions on Cognitive and Developmental Systems, PP (99), 1-17. doi: 10.1109/tcds.2025.3635161

Understanding Neurocognition with Deep Learning and MRI: A Systematic Review

2024

Journal Article

Burden of disease scenarios by state in the USA, 2022-50: a forecasting analysis for the Global Burden of Disease Study 2021

Mokdad, Ali H., Bisignano, Catherine, Hsu, Johnathan M., Bryazka, Dana, Cao, Shujin, Bhattacharjee, Natalia V., Dalton, Bronte E., Lindstedt, Paulina A., Smith, Amanda E., Ababneh, Hazim S., Abbasgholizadeh, Rouzbeh, Abdelkader, Atef, Abdi, Parsa, Abiodun, Olugbenga Olusola, Aboagye, Richard Gyan, Abukhadijah, Hana J., Abu-Zaid, Ahmed, Acuna, Juan Manuel, Addo, Isaac Yeboah, Adekanmbi, Victor, Adeyeoluwa, Temitayo Esther, Adzigbli, Leticia Akua, Afolabi, Aanuoluwapo Adeyimika, Afrashteh, Fatemeh, Agyemang-Duah, Williams, Ahmad, Shahzaib, Ahmadzade, Mohadese, Ahmed, Ali, Ahmed, Ayman ... Murray, Christopher J. L. (2024). Burden of disease scenarios by state in the USA, 2022-50: a forecasting analysis for the Global Burden of Disease Study 2021. Lancet, 404 (10469), 2341-2370. doi: 10.1016/S0140-6736(24)02246-3

Burden of disease scenarios by state in the USA, 2022-50: a forecasting analysis for the Global Burden of Disease Study 2021

2024

Journal Article

Burden of disease scenarios by state in the USA, 2022-50: a forecasting analysis for the Global Burden of Disease Study 2021

Mokdad, Ali H., Bisignano, Catherine, Hsu, Johnathan M., Bryazka, Dana, Cao, Shujin, Bhattacharjee, Natalia V., Dalton, Bronte E., Lindstedt, Paulina A., Smith, Amanda E., Ababneh, Hazim S., Abbasgholizadeh, Rouzbeh, Abdelkader, Atef, Abdi, Parsa, Abiodun, Olugbenga Olusola, Aboagye, Richard Gyan, Abukhadijah, Hana J., Abu-Zaid, Ahmed, Acuna, Juan Manuel, Addo, Isaac Yeboah, Adekanmbi, Victor, Adeyeoluwa, Temitayo Esther, Adzigbli, Leticia Akua, Afolabi, Aanuoluwapo Adeyimika, Afrashteh, Fatemeh, Agyemang-Duah, Williams, Ahmad, Shahzaib, Ahmadzade, Mohadese, Ahmed, Ali, Ahmed, Ayman ... Murray, Christopher J. L. (2024). Burden of disease scenarios by state in the USA, 2022-50: a forecasting analysis for the Global Burden of Disease Study 2021. Lancet, 404 (10469), 2341-2370.

Burden of disease scenarios by state in the USA, 2022-50: a forecasting analysis for the Global Burden of Disease Study 2021

2024

Journal Article

The burden of diseases, injuries, and risk factors by state in the USA, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Mokdad, Ali H, Bisignano, Catherine, Hsu, Johnathan M, Aldridge, Robert W, Aravkin, Aleksandr Y, Brauer, Michael, Bryazka, Dana, Cagney, Jack, Cogen, Rebecca M, Culbreth, Garland T, Dai, Xiaochen, Daoud, Farah, Degenhardt, Louisa, Dwyer-Lindgren, Laura, Feigin, Valery L, Flor, Luisa S, Fu, Weijia, Gardner, William M, Haakenstad, Annie, Haile, Demewoz, Hamilton, Erin B, Hay, Simon I, Ikuta, Kevin S, Kassebaum, Nicholas J, Lim, Stephen S, Mestrovic, Tomislav, Moberg, Madeline E, Mougin, Vincent, Naghavi, Mohsen ... Zyoud, Sa'ed H (2024). The burden of diseases, injuries, and risk factors by state in the USA, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet, 404 (10469), 2314-2340. doi: 10.1016/S0140-6736(24)01446-6

The burden of diseases, injuries, and risk factors by state in the USA, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

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

  • 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

    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

    Robust and Explainable AI to Solve Clinical Problems

    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

Completed supervision

Media

Enquiries

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