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)

241 - 260 of 367 works

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

Designing a multi-epitope vaccine candidate to combat MERS-CoV by employing an immunoinformatics approach

Mahmud, Shafi, Rafi, Md. Oliullah, Paul, Gobindo Kumar, Promi, Maria Meha, Shimu, Mst. Sharmin Sultana, Biswas, Suvro, Emran, Talha Bin, Dhama, Kuldeep, Alyami, Salem A., Moni, Mohammad Ali and Saleh, Md. Abu (2021). Designing a multi-epitope vaccine candidate to combat MERS-CoV by employing an immunoinformatics approach. Scientific Reports, 11 (1) 15431, 1-20. doi: 10.1038/s41598-021-92176-1

Designing a multi-epitope vaccine candidate to combat MERS-CoV by employing an immunoinformatics approach

2021

Journal Article

Structural and functional elucidation of IF-3 protein of Chloroflexus aurantiacus involved in protein biosynthesis: an In Silico approach

Saikat, Abu Saim Mohammad, Uddin, Md. Ekhlas, Ahmad, Tasnim, Mahmud, Shahriar, Imran, Md. Abu Sayeed, Ahmed, Sohel, Alyami, Salem A. and Moni, Mohammad Ali (2021). Structural and functional elucidation of IF-3 protein of Chloroflexus aurantiacus involved in protein biosynthesis: an In Silico approach. BioMed Research International, 2021 (1) 9050026, 1-10. doi: 10.1155/2021/9050026

Structural and functional elucidation of IF-3 protein of Chloroflexus aurantiacus involved in protein biosynthesis: an In Silico approach

2021

Journal Article

C-C Chemokine receptor-like 2 (CCRL2) acts as coreceptor for human immunodeficiency virus-2

Islam, Salequl, Moni, Mohammad Ali, Urmi, Umme Laila, Tanaka, Atsushi and Hoshino, Hiroo (2021). C-C Chemokine receptor-like 2 (CCRL2) acts as coreceptor for human immunodeficiency virus-2. Briefings in Bioinformatics, 22 (4). doi: 10.1093/bib/bbaa333

C-C Chemokine receptor-like 2 (CCRL2) acts as coreceptor for human immunodeficiency virus-2

2021

Journal Article

SCNN: Scalogram-based convolutional neural network to detect obstructive sleep apnea using single-lead electrocardiogram signals

Mashrur, Fazla Rabbi, Islam, Md. Saiful, Saha, Dabasish Kumar, Islam, S.M. Riazul and Moni, Mohammad Ali (2021). SCNN: Scalogram-based convolutional neural network to detect obstructive sleep apnea using single-lead electrocardiogram signals. Computers in Biology and Medicine, 134 104532, 104532. doi: 10.1016/j.compbiomed.2021.104532

SCNN: Scalogram-based convolutional neural network to detect obstructive sleep apnea using single-lead electrocardiogram signals

2021

Journal Article

A human-robot interaction system calculating visual focus of human’s attention level

Chakraborty, Partha, Ahmed, Sabbir, Yousuf, Mohammad Abu, Azad, Akm, Alyami, Salem A. and Moni, Mohammad Ali (2021). A human-robot interaction system calculating visual focus of human’s attention level. IEEE Access, 9 9462086, 93409-93421. doi: 10.1109/access.2021.3091642

A human-robot interaction system calculating visual focus of human’s attention level

2021

Journal Article

Emotion recognition from EEG signal focusing on deep learning and shallow learning techniques

Islam, Md. Rabiul, Moni, Mohammad Ali, Islam, Md. Milon, Rashed-Al-Mahfuz, Md., Islam, Md. Saiful, Hasan, Md. Kamrul, Hossain, Md. Sabir, Ahmad, Mohiuddin, Uddin, Shahadat, Azad, Akm, Alyami, Salem A., Ahad, Md. Atiqur Rahman and Lio, Pietro (2021). Emotion recognition from EEG signal focusing on deep learning and shallow learning techniques. IEEE Access, 9 9462089, 94601-94624. doi: 10.1109/access.2021.3091487

Emotion recognition from EEG signal focusing on deep learning and shallow learning techniques

2021

Journal Article

Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019

Reitsma, Marissa B, Kendrick, Parkes J, Ababneh, Emad, Abbafati, Cristiana, Abbasi-Kangevari, Mohsen, Abdoli, Amir, Abedi, Aidin, Abhilash, E. S., Abila, Derrick Bary, Aboyans, Victor, Abu-Rmeileh, Niveen ME, Adebayo, Oladimeji M, Advani, Shailesh M, Aghaali, Mohammad, Ahinkorah, Bright Opoku, Ahmad, Sohail, Ahmadi, Keivan, Ahmed, Haroon, Aji, Budi, Akunna, Chisom Joyqueenet, Al-Aly, Ziyad, Alanzi, Turki M, Alhabib, Khalid F, Ali, Liaqat, Alif, Sheikh Mohammad, Alipour, Vahid, Aljunid, Syed Mohamed, Alla, François, Allebeck, Peter ... Zuniga, Y. H. (2021). Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019. The Lancet, 397 (10292), 2337-2360. doi: 10.1016/S0140-6736(21)01169-7

Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019

2021

Journal Article

Significant pathway and biomarker identification of pancreatic cancer associated lung cancer

Khan, Tamanna, Paul, Bikash Kumar, Hasan, Md Tanvir, Islam, Md Rakib, Arefin, M. A., Ahmed, K., Islam, Md K. and Moni, Mohammad Ali (2021). Significant pathway and biomarker identification of pancreatic cancer associated lung cancer. Informatics in Medicine Unlocked, 25 100637, 1-8. doi: 10.1016/j.imu.2021.100637

Significant pathway and biomarker identification of pancreatic cancer associated lung cancer

2021

Journal Article

Transcriptomic studies revealed pathophysiological impact of COVID-19 to predominant health conditions

Nain, Zulkar, Barman, Shital K, Sheam, Md Moinuddin, Syed, Shifath Bin, Samad, Abdus, Quinn, Julian M W, Karim, Mohammad Minnatul, Himel, Mahbubul Kabir, Roy, Rajib Kanti, Moni, Mohammad Ali and Biswas, Sudhangshu Kumar (2021). Transcriptomic studies revealed pathophysiological impact of COVID-19 to predominant health conditions. Briefings in Bioinformatics, 22 (6) bbab197. doi: 10.1093/bib/bbab197

Transcriptomic studies revealed pathophysiological impact of COVID-19 to predominant health conditions

2021

Journal Article

Public health utility of cause of death data: applying empirical algorithms to improve data quality

Johnson, Sarah Charlotte, Cunningham, Matthew, Dippenaar, Ilse N., Sharara, Fablina, Wool, Eve E., Agesa, Kareha M., Han, Chieh, Miller-Petrie, Molly K., Wilson, Shadrach, Fuller, John E., Balassyano, Shelly, Bertolacci, Gregory J., Davis Weaver, Nicole, Arabloo, Jalal, Badawi, Alaa, Bhagavathula, Akshaya Srikanth, Burkart, Katrin, Cámera, Luis Alberto, Carvalho, Felix, Castañeda-Orjuela, Carlos A., Choi, Jee-Young Jasmine, Chu, Dinh-Toi, Dai, Xiaochen, Dianatinasab, Mostafa, Emmons-Bell, Sophia, Fernandes, Eduarda, Fischer, Florian, Ghashghaee, Ahmad, Golechha, Mahaveer ... Naghavi, Mohsen (2021). Public health utility of cause of death data: applying empirical algorithms to improve data quality. BMC Medical Informatics and Decision Making, 21 (1) 175. doi: 10.1186/s12911-021-01501-1

Public health utility of cause of death data: applying empirical algorithms to improve data quality

2021

Journal Article

Healthcare seeking behavior and glycemic control in patients with type 2 diabetes attending a tertiary hospital

Islam, Sheikh Mohammed Shariful, Uddin, Riaz, Zaman, Sojib Bin, Biswas, Tuhin, Tansi, Tania, Chegini, Zahra, Moni, Mohammad Ali, Niessen, Louis and Naheed, Aliya (2021). Healthcare seeking behavior and glycemic control in patients with type 2 diabetes attending a tertiary hospital. International Journal of Diabetes in Developing Countries, 41 (2), 280-287. doi: 10.1007/s13410-020-00875-8

Healthcare seeking behavior and glycemic control in patients with type 2 diabetes attending a tertiary hospital

2021

Journal Article

Subnational mapping of HIV incidence and mortality among individuals aged 15-49 years in sub-Saharan Africa, 2000-18: a modelling study

Sartorius, Benn, Van der Heide, John, Yang, Mingyou, Goosmann, Erik, Hon, Julia, Haeuser, Emily, Cork, Michael, Perkins, Samantha, Jahagirdar, Deepa, Schaeffer, Lauren, Serfes, Audrey, LeGrand, Kate, Abbastabar, Hedayat, Abebo, Zeleke, Abosetugn, Akine, Abu-Gharbieh, Eman, Accrombessi, Manfred, Adebayo, Oladimeji, Adegbosin, Adeyinka, Adekanmbi, Victor, Adetokunboh, Olatunji, Adeyinka, Daniel, Ahinkorah, Bright, Ahmadi, Keivan, Ahmed, Muktar, Akalu, Yonas, Akinyemi, Oluwaseun, Akinyemi, Rufus, Aklilu, Addis ... Dwyer-Lindgren, Laura (2021). Subnational mapping of HIV incidence and mortality among individuals aged 15-49 years in sub-Saharan Africa, 2000-18: a modelling study. The Lancet HIV, 8 (6), E363-E375.

Subnational mapping of HIV incidence and mortality among individuals aged 15-49 years in sub-Saharan Africa, 2000-18: a modelling study

2021

Journal Article

Lung cancer detection using enhanced segmentation accuracy

Akter, Onika, Moni, Mohammad Ali, Islam, Mohammad Mahfuzul, Quinn, Julian M. W. and Kamal, A. H.M. (2021). Lung cancer detection using enhanced segmentation accuracy. Applied Intelligence, 51 (6), 3391-3404. doi: 10.1007/s10489-020-02046-y

Lung cancer detection using enhanced segmentation accuracy

2021

Journal Article

COVID-19 patient transcriptomic and genomic profiling reveals comorbidity interactions with psychiatric disorders

Moni, Mohammad Ali, Lin, Ping-I, Quinn, Julian M. W. and Eapen, Valsamma (2021). COVID-19 patient transcriptomic and genomic profiling reveals comorbidity interactions with psychiatric disorders. Translational Psychiatry, 11 (1) 160, 1-13. doi: 10.1038/s41398-020-01151-3

COVID-19 patient transcriptomic and genomic profiling reveals comorbidity interactions with psychiatric disorders

2021

Journal Article

Improved transfer-learning-based facial recognition framework to detect autistic children at an early stage

Akter, Tania, Ali, Mohammad Hanif, Khan, Md. Imran, Satu, Md. Shahriare, Uddin, Md. Jamal, Alyami, Salem A., Ali, Sarwar, Azad, AKM and Moni, Mohammad Ali (2021). Improved transfer-learning-based facial recognition framework to detect autistic children at an early stage. Brain Sciences, 11 (6) 734, 734. doi: 10.3390/brainsci11060734

Improved transfer-learning-based facial recognition framework to detect autistic children at an early stage

2021

Journal Article

Spatial, temporal, and demographic patterns in prevalence of chewing tobacco use in 204 countries and territories, 1990-2019: a systematic analysis from the Global Burden of Disease Study 2019

GBD 2019 Chewing Tobacco Collaborators, Wubishet, Befikadu Legesse, Mamun, Abdullah A. and Moni, Mohammad Ali (2021). Spatial, temporal, and demographic patterns in prevalence of chewing tobacco use in 204 countries and territories, 1990-2019: a systematic analysis from the Global Burden of Disease Study 2019. The Lancet Public health, 6 (7), e482-e499. doi: 10.1016/S2468-2667(21)00065-7

Spatial, temporal, and demographic patterns in prevalence of chewing tobacco use in 204 countries and territories, 1990-2019: a systematic analysis from the Global Burden of Disease Study 2019

2021

Journal Article

Identifying subgroups of patients with autism by gene expression profiles using machine learning algorithms

Lin, Ping-I, Moni, Mohammad Ali, Gau, Susan Shur-Fen and Eapen, Valsamma (2021). Identifying subgroups of patients with autism by gene expression profiles using machine learning algorithms. Frontiers in Psychiatry, 12 637022. doi: 10.3389/fpsyt.2021.637022

Identifying subgroups of patients with autism by gene expression profiles using machine learning algorithms

2021

Journal Article

Short-term prediction of COVID-19 cases using machine learning models

Satu, Md. Shahriare, Howlader, Koushik Chandra, Mahmud, Mufti, Kaiser, M. Shamim, Shariful Islam, Sheikh Mohammad, Quinn, Julian M. W., Alyami, Salem A. and Moni, Mohammad Ali (2021). Short-term prediction of COVID-19 cases using machine learning models. Applied Sciences, 11 (9) 4266, 4266. doi: 10.3390/app11094266

Short-term prediction of COVID-19 cases using machine learning models

2021

Journal Article

Diverse immunological factors influencing pathogenesis in patients with COVID-19: a review on viral dissemination, immunotherapeutic options to counter cytokine storm and inflammatory responses

Rabaan, Ali A., Al-Ahmed, Shamsah H., Garout, Mohammed A., Al-Qaaneh, Ayman M., Sule, Anupam A, Tirupathi, Raghavendra, Mutair, Abbas Al, Alhumaid, Saad, Hasan, Abdulkarim, Dhawan, Manish, Tiwari, Ruchi, Sharun, Khan, Mohapatra, Ranjan K., Mitra, Saikat, Emran, Talha Bin, Bilal, Muhammad, Singh, Rajendra, Alyami, Salem A., Moni, Mohammad Ali and Dhama, Kuldeep (2021). Diverse immunological factors influencing pathogenesis in patients with COVID-19: a review on viral dissemination, immunotherapeutic options to counter cytokine storm and inflammatory responses. Pathogens, 10 (5) 565, 565. doi: 10.3390/pathogens10050565

Diverse immunological factors influencing pathogenesis in patients with COVID-19: a review on viral dissemination, immunotherapeutic options to counter cytokine storm and inflammatory responses

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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