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

397 works between 2012 and 2026
All (397) Book (2) Journal Article (339) Conference Publication (52) Book Chapter (4)

161 - 180 of 397 works

2023

Journal Article

Preface

Satu, Md. Shahriare, Moni, Mohammad Ali, Kaiser, M. Shamim and Arefin, Mohammad Shamsul (2023). Preface. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 490 LNICST, v-vi.

Preface

2023

Journal Article

A comprehensive review of green computing: past, present, and future research

Paul, Showmick Guha, Saha, Arpa, Arefin, Mohammad Shamsul, Bhuiyan, Touhid, Biswas, Al Amin, Reza, Ahmed Wasif, Alotaibi, Naif M., Alyami, Salem A. and Moni, Mohammad Ali (2023). A comprehensive review of green computing: past, present, and future research. IEEE Access, 11, 1-1. doi: 10.1109/access.2023.3304332

A comprehensive review of green computing: past, present, and future research

2023

Book

Machine intelligence and emerging technologies : first international conference, MIET 2022, Noakhali, Bangladesh, September 23-25, 2022, Proceedings, Part II

Md. Shahriare Satu, Mohammad Ali Moni, M. Shamim Kaiser and Mohammad Shamsul Arefin eds. (2023). Machine intelligence and emerging technologies : first international conference, MIET 2022, Noakhali, Bangladesh, September 23-25, 2022, Proceedings, Part II. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-34622-4

Machine intelligence and emerging technologies : first international conference, MIET 2022, Noakhali, Bangladesh, September 23-25, 2022, Proceedings, Part II

2023

Journal Article

Adverse effects of COVID-19 vaccination: machine learning and statistical approach to identify and classify incidences of morbidity and postvaccination reactogenicity

Ahamad, Md. Martuza, Aktar, Sakifa, Uddin, Md. Jamal, Rashed-Al-Mahfuz, Md., Azad, A. K. M., Uddin, Shahadat, Alyami, Salem A., Sarker, Iqbal H., Khan, Asaduzzaman, Liò, Pietro, Quinn, Julian M. W. and Moni, Mohammad Ali (2023). Adverse effects of COVID-19 vaccination: machine learning and statistical approach to identify and classify incidences of morbidity and postvaccination reactogenicity. Healthcare, 11 (1) 31, 31. doi: 10.3390/healthcare11010031

Adverse effects of COVID-19 vaccination: machine learning and statistical approach to identify and classify incidences of morbidity and postvaccination reactogenicity

2022

Journal Article

Metastatic phenotype and immunosuppressive tumour microenvironment in pancreatic ductal adenocarcinoma: key role of the urokinase plasminogen activator (PLAU)

Hosen, S. M. Zahid, Uddin, Md. Nazim, Xu, Zhihong, Buckley, Benjamin J., Perera, Chamini, Pang, Tony C. Y., Mekapogu, Alpha Raj, Moni, Mohammad Ali, Notta, Faiyaz, Gallinger, Steven, Pirola, Ron, Wilson, Jeremy, Ranson, Marie, Goldstein, David and Apte, Minoti (2022). Metastatic phenotype and immunosuppressive tumour microenvironment in pancreatic ductal adenocarcinoma: key role of the urokinase plasminogen activator (PLAU). Frontiers in Immunology, 13 1060957, 1060957. doi: 10.3389/fimmu.2022.1060957

Metastatic phenotype and immunosuppressive tumour microenvironment in pancreatic ductal adenocarcinoma: key role of the urokinase plasminogen activator (PLAU)

2022

Journal Article

AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning

Charoenkwan, Phasit, Ahmed, Saeed, Nantasenamat, Chanin, Quinn, Julian M. W., Moni, Mohammad Ali, Lio’, Pietro and Shoombuatong, Watshara (2022). AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning. Scientific Reports, 12 (1) 7697, 7697. doi: 10.1038/s41598-022-11897-z

AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning

2022

Journal Article

StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy

Schaduangrat, Nalini, Anuwongcharoen, Nuttapat, Moni, Mohammad Ali, Lio’, Pietro, Charoenkwan, Phasit and Shoombuatong, Watshara (2022). StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy. Scientific Reports, 12 (1) 16435, 1-16. doi: 10.1038/s41598-022-20143-5

StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy

2022

Journal Article

Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images

Uddin, Khandaker Mohammad Mohi, Dey, Samrat Kumar, Babu, Hafiz Md. Hasan, Mostafiz, Rafid, Uddin, Shahadat, Shoombuatong, Watshara and Moni, Mohammad Ali (2022). Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images. Scientific Reports, 12 (1) 21796, 1-15. doi: 10.1038/s41598-022-25539-x

Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images

2022

Journal Article

Identification of glycophorin C as a prognostic marker for human breast cancer using bioinformatic analysis

Rahman, Md. Shahedur, Biswas, Polash Kumar, Saha, Subbroto Kumar and Moni, Mohammad Ali (2022). Identification of glycophorin C as a prognostic marker for human breast cancer using bioinformatic analysis. Network Modeling Analysis in Health Informatics and Bioinformatics, 11 (1) 7. doi: 10.1007/s13721-021-00352-0

Identification of glycophorin C as a prognostic marker for human breast cancer using bioinformatic analysis

2022

Journal Article

EEG-based emotion analysis using non-linear features and ensemble learning approaches

Rahman, Md. Mustafizur, Sarkar, Ajay Krishno, Hossain, Md. Amzad and Moni, Mohammad Ali (2022). EEG-based emotion analysis using non-linear features and ensemble learning approaches. Expert Systems with Applications, 207 118025, 1-26. doi: 10.1016/j.eswa.2022.118025

EEG-based emotion analysis using non-linear features and ensemble learning approaches

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

Availability

Dr Mohammad Ali Moni is:
Available for supervision

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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

  • Master Philosophy

    Quantum Deep Learning for Brain Informatics

    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

Enquiries

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