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

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

141 - 160 of 367 works

2022

Journal Article

iAMAP-SCM: a novel computational tool for large-scale identification of antimalarial peptides using estimated propensity scores of dipeptides

Charoenkwan, Phasit, Schaduangrat, Nalini, Lio, Pietro, Moni, Mohammad Ali, Chumnanpuen, Pramote and Shoombuatong, Watshara (2022). iAMAP-SCM: a novel computational tool for large-scale identification of antimalarial peptides using estimated propensity scores of dipeptides. ACS Omega, 7 (45), 41082-41095. doi: 10.1021/acsomega.2c04465

iAMAP-SCM: a novel computational tool for large-scale identification of antimalarial peptides using estimated propensity scores of dipeptides

2022

Journal Article

Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning

Talukder, Md. Alamin, Islam, Md. Manowarul, Uddin, Md Ashraf, Akhter, Arnisha, Hasan, Khondokar Fida and Moni, Mohammad Ali (2022). Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning. Expert Systems with Applications, 205 117695, 117695. doi: 10.1016/j.eswa.2022.117695

Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning

2022

Journal Article

The burden of bacterial antimicrobial resistance in the WHO European region in 2019: a cross-country systematic analysis

Mestrovic, Tomislav, Robles Aguilar, Gisela, Swetschinski, Lucien R, Ikuta, Kevin S, Gray, Authia P, Davis Weaver, Nicole, Han, Chieh, Wool, Eve E, Gershberg Hayoon, Anna, Hay, Simon I, Dolecek, Christiane, Sartorius, Benn, Murray, Christopher J L, Addo, Isaac Yeboah, Ahinkorah, Bright Opoku, Ahmed, Ayman, Aldeyab, Mamoon A, Allel, Kasim, Ancuceanu, Robert, Anyasodor, Anayochukwu Edward, Ausloos, Marcel, Barra, Fabio, Bhagavathula, Akshaya Srikanth, Bhandari, Dinesh, Bhaskar, Sonu, Cruz-Martins, Natália, Dastiridou, Anna, Dokova, Klara, Dubljanin, Eleonora ... Naghavi, Mohsen (2022). The burden of bacterial antimicrobial resistance in the WHO European region in 2019: a cross-country systematic analysis. The Lancet Public Health, 7 (11), e897-e913. doi: 10.1016/s2468-2667(22)00225-0

The burden of bacterial antimicrobial resistance in the WHO European region in 2019: a cross-country systematic analysis

2022

Journal Article

Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides

Charoenkwan, Phasit, Chumnanpuen, Pramote, Schaduangrat, Nalini, Lio’, Pietro, Moni, Mohammad Ali and Shoombuatong, Watshara (2022). Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides. Journal of Computer - Aided Molecular Design, 36 (11), 781-796. doi: 10.1007/s10822-022-00476-z

Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides

2022

Journal Article

Novel algorithm for multi-time data implantation in a special cyber-manufacturing architecture

Nahar, Mahbubun, Kamal, A. H. M., Hassan, Md Rafiul and Moni, Mohammad Ali (2022). Novel algorithm for multi-time data implantation in a special cyber-manufacturing architecture. Algorithms, 15 (10) 335, 335. doi: 10.3390/a15100335

Novel algorithm for multi-time data implantation in a special cyber-manufacturing architecture

2022

Journal Article

Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework

Charoenkwan, Phasit, Schaduangrat, Nalini, Lio’, Pietro, Moni, Mohammad Ali, Shoombuatong, Watshara and Manavalan, Balachandran (2022). Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework. iScience, 25 (9) 104883, 1-15. doi: 10.1016/j.isci.2022.104883

Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework

2022

Journal Article

Prospects of integrated multi-omics-driven biomarkers for efficient hair loss therapy from systems biology perspective

Yilmaz, Dilan Nisa, Onluturk Aydogan, Ozge, Kori, Medi, Aydin, Busra, Rahman, Md. Rezanur, Moni, Mohammad Ali and Turanli, Beste (2022). Prospects of integrated multi-omics-driven biomarkers for efficient hair loss therapy from systems biology perspective. Gene Reports, 28 101657, 1-9. doi: 10.1016/j.genrep.2022.101657

Prospects of integrated multi-omics-driven biomarkers for efficient hair loss therapy from systems biology perspective

2022

Journal Article

SCMRSA: A new approach for identifying and analyzing anti-MRSA peptides using estimated propensity scores of dipeptides

Charoenkwan, Phasit, Kanthawong, Sakawrat, Schaduangrat, Nalini, Li', Pietro, Moni, Mohammad Ali and Shoombuatong, Watshara (2022). SCMRSA: A new approach for identifying and analyzing anti-MRSA peptides using estimated propensity scores of dipeptides. ACS Omega, 7 (36), 32653-32664. doi: 10.1021/acsomega.2c04305

SCMRSA: A new approach for identifying and analyzing anti-MRSA peptides using estimated propensity scores of dipeptides

2022

Journal Article

CNN based on transfer learning models using data augmentation and transformation for detection of concrete crack

Islam, Md. Monirul, Hossain, Md. Belal, Akhtar, Md. Nasim, Moni, Mohammad Ali and Hasan, Khondokar Fida (2022). CNN based on transfer learning models using data augmentation and transformation for detection of concrete crack. Algorithms, 15 (8) 287, 287. doi: 10.3390/a15080287

CNN based on transfer learning models using data augmentation and transformation for detection of concrete crack

2022

Journal Article

Systems biology models to identify the influence of SARS-CoV-2 infections to the progression of human autoimmune diseases

Al-Mustanjid, Md, Mahmud, S. M. Hasan, Akter, Farzana, Rahman, Md Shazzadur, Hossen, Md Sajid, Rahman, Md Habibur and Moni, Mohammad Ali (2022). Systems biology models to identify the influence of SARS-CoV-2 infections to the progression of human autoimmune diseases. Informatics in Medicine Unlocked, 32 101003, 1-15. doi: 10.1016/j.imu.2022.101003

Systems biology models to identify the influence of SARS-CoV-2 infections to the progression of human autoimmune diseases

2022

Journal Article

Adolescent transport and unintentional injuries: a systematic analysis using the Global Burden of Disease Study 2019

Peden, Amy E, Cullen, Patricia, Francis, Kate Louise, Moeller, Holger, Peden, Margaret M, Ye, Pengpeng, Tian, Maoyi, Zou, Zhiyong, Sawyer, Susan M, Aali, Amirali, Abbasi-Kangevari, Zeinab, Abbasi-Kangevari, Mohsen, Abdelmasseh, Michael, Abdoun, Meriem, Abd-Rabu, Rami, Abdulah, Deldar Morad, Abebe, Getachew, Abebe, Ayele Mamo, Abedi, Aidin, Abidi, Hassan, Aboagye, Richard Gyan, Abubaker Ali, Hiwa, Abu-Gharbieh, Eman, Adane, Denberu Eshetie, Adane, Tigist Demssew, Addo, Isaac Yeboah, Adewole, Ololade Grace, Adhikari, Sangeet, Adnan, Mohammad ... Ivers, Rebecca Q (2022). Adolescent transport and unintentional injuries: a systematic analysis using the Global Burden of Disease Study 2019. The Lancet Public Health, 7 (8), e657-e669. doi: 10.1016/S2468-2667(22)00134-7

Adolescent transport and unintentional injuries: a systematic analysis using the Global Burden of Disease Study 2019

2022

Journal Article

Bioinformatics strategies to identify shared molecular biomarkers that link ischemic stroke and moyamoya disease with glioblastoma

Islam, Md Khairul, Islam, Md Rakibul, Rahman, Md Habibur, Islam, Md Zahidul, Amin, Md Al, Ahmed, Kazi Rejvee, Rahman, Md Ataur, Moni, Mohammad Ali and Kim, Bonglee (2022). Bioinformatics strategies to identify shared molecular biomarkers that link ischemic stroke and moyamoya disease with glioblastoma. Pharmaceutics, 14 (8) 1573, 1573. doi: 10.3390/pharmaceutics14081573

Bioinformatics strategies to identify shared molecular biomarkers that link ischemic stroke and moyamoya disease with glioblastoma

2022

Journal Article

Early-stage detection of ovarian cancer based on clinical data using machine learning approaches

Ahamad, Md. Martuza, Aktar, Sakifa, Uddin, Md. Jamal, Rahman, Tasnia, Alyami, Salem A., Al-Ashhab, Samer, Akhdar, Hanan Fawaz, Azad, AKM and Moni, Mohammad Ali (2022). Early-stage detection of ovarian cancer based on clinical data using machine learning approaches. Journal of Personalized Medicine, 12 (8) 1211, 1211. doi: 10.3390/jpm12081211

Early-stage detection of ovarian cancer based on clinical data using machine learning approaches

2022

Journal Article

Genetic pathways associated with sleep problems in children with autism spectrum disorder

Lin, Ping-I, Masi, Anne, Moni, Mohammad Ali, Kummerfeld, Sarah and Eapen, Valsamma (2022). Genetic pathways associated with sleep problems in children with autism spectrum disorder. Frontiers in Psychiatry, 13 904091, 1-8. doi: 10.3389/fpsyt.2022.904091

Genetic pathways associated with sleep problems in children with autism spectrum disorder

2022

Journal Article

GreenMolBD: nature derived bioactive molecules' database

Zahid Hosen, S. M., Junaid, Md., Alam, Muhammad Shaiful, Rubayed, Maruf, Dash, Raju, Akter, Rasheda, Sharmin, Tania, Mouri, Nusrat Jahan, Moni, Mohammad Ali, Khatun, Mahmuda and Mostafa, Mohammad (2022). GreenMolBD: nature derived bioactive molecules' database. Medicinal Chemistry, 18 (6), 724-733. doi: 10.2174/1573406418666211129103458

GreenMolBD: nature derived bioactive molecules' database

2022

Journal Article

A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models

Aurna, Nahid Ferdous, Yousuf, Mohammad Abu, Taher, Kazi Abu, Azad, A.K.M. and Moni, Mohammad Ali (2022). A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models. Computers in Biology and Medicine, 146 105539, 105539. doi: 10.1016/j.compbiomed.2022.105539

A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models

2022

Journal Article

SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins

Charoenkwan, Phasit, Schaduangrat, Nalini, Moni, Mohammad Ali, Lio’, Pietro, Manavalan, Balachandran and Shoombuatong, Watshara (2022). SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins. Computers in Biology and Medicine, 146 105704, 105704. doi: 10.1016/j.compbiomed.2022.105704

SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins

2022

Journal Article

Clinical and behavioral attributes leading to sleep disorders in children on the autism spectrum

Masi, Anne, Moni, Mohammod Ali, Azim, Syeda Ishra, Choi, Byungkuk, Heussler, Helen, Lin, Ping-I, Diaz, Antonio Mendoza and Eapen, Valsamma (2022). Clinical and behavioral attributes leading to sleep disorders in children on the autism spectrum. Autism Research, 15 (7), 1274-1287. doi: 10.1002/aur.2745

Clinical and behavioral attributes leading to sleep disorders in children on the autism spectrum

2022

Journal Article

Hydrogel nanoarchitectonics: an evolving paradigm for ultrasensitive biosensing

Nishat, Zakia Sultana, Hossain, Tanvir, Islam, Md. Nazmul, Phan, Hoang‐Phuong, Wahab, Md A., Moni, Mohammad Ali, Salomon, Carlos, Amin, Mohammed A., Sina, Abu Ali Ibn, Hossain, Md Shahriar A., Kaneti, Yusuf Valentino, Yamauchi, Yusuke and Masud, Mostafa Kamal (2022). Hydrogel nanoarchitectonics: an evolving paradigm for ultrasensitive biosensing. Small, 18 (26) 2107571, 2107571. doi: 10.1002/smll.202107571

Hydrogel nanoarchitectonics: an evolving paradigm for ultrasensitive biosensing

2022

Journal Article

Patterns of sensory modulation by age and sex in young people on the autism spectrum

Lane, Alison E., Simpson, Kate, Masi, Anne, Grove, Rachel, Moni, Mohammad Ali, Montgomery, Alicia, Roberts, Jacqui, Silove, Natalie, Whalen, Olivia, Whitehouse, Andrew J. O. and Eapen, Valsamma (2022). Patterns of sensory modulation by age and sex in young people on the autism spectrum. Autism Research, 15 (10), 1840-1854. doi: 10.1002/aur.2762

Patterns of sensory modulation by age and sex in young people on the autism spectrum

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

    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

    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

    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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