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

323 works between 2012 and 2024
All (323) Journal Article (282) Book Chapter (4) Conference Publication (36) Book (1)

81 - 100 of 323 works

2023

Journal Article

Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke

Islam, Tania, Rahman, Md Rezanur, Khan, Asaduzzaman and Moni, Mohammad Ali (2023). Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke. Journal of Biomedical Informatics, 141 104345, 1-13. doi: 10.1016/j.jbi.2023.104345

Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke

2023

Journal Article

An integrated statistical and clinically applicable machine learning framework for the detection of autism spectrum disorder

Uddin, Md. Jamal, Ahamad, Md. Martuza, Sarker, Prodip Kumar, Aktar, Sakifa, Alotaibi, Naif, Alyami, Salem A., Kabir, Muhammad Ashad and Moni, Mohammad Ali (2023). An integrated statistical and clinically applicable machine learning framework for the detection of autism spectrum disorder. Computers, 12 (5) 92, 1-19. doi: 10.3390/computers12050092

An integrated statistical and clinically applicable machine learning framework for the detection of autism spectrum disorder

2023

Journal Article

GRU-INC: an inception-attention based approach using GRU for human activity recognition

Mim, Taima Rahman, Amatullah, Maliha, Afreen, Sadia, Yousuf, Mohammad Abu, Uddin, Shahadat, Alyami, Salem A., Hasan, Khondokar Fida and Moni, Mohammad Ali (2023). GRU-INC: an inception-attention based approach using GRU for human activity recognition. Expert Systems with Applications, 216 119419, 119419. doi: 10.1016/j.eswa.2022.119419

GRU-INC: an inception-attention based approach using GRU for human activity recognition

2023

Journal Article

Multi-omics data integration methods and their applications in psychiatric disorders

Sathyanarayanan, Anita, Mueller, Tamara T., Ali Moni, Mohammad, Schueler, Katja, Baune, Bernhard T., Lio, Pietro, Mehta, Divya, Baune, Bernhard T, Dierssen, Mara, Ebert, Bjarke, Fabbri, Chiara, Fusar-Poli, Paolo, Gennarelli, Massimo, Harmer, Catherine, Howes, Oliver D., Janzing, Joost G.E., Lio, Pietro, Maron, Eduard, Mehta, Divya, Minelli, Alessandra, Nonell, Lara, Pisanu, Claudia, Potier, Marie-Claude, Rybakowski, Filip, Serretti, Alessandro, Sqassina, Alessio, Stacey, David, van Westrhenen, Roos and Xicota, Laura (2023). Multi-omics data integration methods and their applications in psychiatric disorders. European Neuropsychopharmacology, 69, 26-46. doi: 10.1016/j.euroneuro.2023.01.001

Multi-omics data integration methods and their applications in psychiatric disorders

2023

Journal Article

An integrated in-silico Pharmaco-BioInformatics approaches to identify synergistic effects of COVID-19 to HIV patients

Hossain, Md Arju, Rahman, Md Habibur, Sultana, Habiba, Ahsan, Asif, Rayhan, Saiful Islam, Hasan, Md Imran, Sohel, Md, Somadder, Pratul Dipta and Moni, Mohammad Ali (2023). An integrated in-silico Pharmaco-BioInformatics approaches to identify synergistic effects of COVID-19 to HIV patients. Computers in Biology and Medicine, 155 106656, 1-21. doi: 10.1016/j.compbiomed.2023.106656

An integrated in-silico Pharmaco-BioInformatics approaches to identify synergistic effects of COVID-19 to HIV patients

2023

Conference Publication

Deep CNN-GRU based human activity recognition with automatic feature extraction using smartphone and wearable sensors

Khatun, Mst. Alema, Yousuf, Mohammad Abu and Moni, Mohammad Ali (2023). Deep CNN-GRU based human activity recognition with automatic feature extraction using smartphone and wearable sensors. 3rd International Conference on Electrical, Computer and Communication Engineering, ECCE 2023, Chittagong, Bangladesh, 23-25 February 2023. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ecce57851.2023.10101550

Deep CNN-GRU based human activity recognition with automatic feature extraction using smartphone and wearable sensors

2023

Book Chapter

IoT and deep learning-based smart healthcare with an integrated security system to detect various skin lesions

Islam, Khairul, Islam, Zahidul, Amin, Al, Akanda, Mojibur Rahman Redoy, Hossen, Shabuj, Naznin, Feroza and Moni, Mohammad Ali (2023). IoT and deep learning-based smart healthcare with an integrated security system to detect various skin lesions. Artificial intelligence for disease diagnosis and prognosis in smart healthcare. (pp. 219-242) edited by Ghita Kouadri Mostefaoui, S. M. Riazul Islam and Faisal Tariq. Boca Raton, FL, United States: CRC Press. doi: 10.1201/9781003251903-13

IoT and deep learning-based smart healthcare with an integrated security system to detect various skin lesions

2023

Journal Article

DTLCx: an improved ResNet architecture to classify normal and conventional pneumonia cases from COVID-19 instances with Grad-CAM-based superimposed visualization utilizing chest X-ray images

Ahamed, Md. Khabir Uddin, Islam, Md Manowarul, Uddin, Md. Ashraf, Akhter, Arnisha, Acharjee, Uzzal Kumar, Paul, Bikash Kumar and Moni, Mohammad Ali (2023). DTLCx: an improved ResNet architecture to classify normal and conventional pneumonia cases from COVID-19 instances with Grad-CAM-based superimposed visualization utilizing chest X-ray images. Diagnostics, 13 (3) 551, 551. doi: 10.3390/diagnostics13030551

DTLCx: an improved ResNet architecture to classify normal and conventional pneumonia cases from COVID-19 instances with Grad-CAM-based superimposed visualization utilizing chest X-ray images

2023

Journal Article

A dependable hybrid machine learning model for network intrusion detection

Talukder, Md. Alamin, Hasan, Khondokar Fida, Islam, Md. Manowarul, Uddin, Md. Ashraf, Akhter, Arnisha, Yousuf, Mohammand Abu, Alharbi, Fares and Moni, Mohammad Ali (2023). A dependable hybrid machine learning model for network intrusion detection. Journal of Information Security and Applications, 72 103405. doi: 10.1016/j.jisa.2022.103405

A dependable hybrid machine learning model for network intrusion detection

2023

Journal Article

Systematic approach to identify therapeutic targets and functional pathways for the cervical cancer

Hasan, Md. Tanvir, Islam, Md. Rakibul, Islam, Md. Rezwan, Altahan, Baraa Riyadh, Ahmed, Kawsar, Bui, Francis M., Azam, Sami and Moni, Mohammad Ali (2023). Systematic approach to identify therapeutic targets and functional pathways for the cervical cancer. Journal of Genetic Engineering and Biotechnology, 21 (1) 10, 10. doi: 10.1186/s43141-023-00469-x

Systematic approach to identify therapeutic targets and functional pathways for the cervical cancer

2023

Book Chapter

Mining significant pre-diabetes features of diabetes mellitus: a case study of Noakhali, Bangladesh

Satu, Md. Shahriare, Howlader, Koushik Chandra, Barua, Avijit and Moni, Mohammad Ali (2023). Mining significant pre-diabetes features of diabetes mellitus: a case study of Noakhali, Bangladesh. Applied informatics for Industry 4.0. (pp. 280-292) edited by Nazmul Siddique, Mohammad Shamsul Arefin, Julie Wall and M. Shamim Kaiser. Boca Raton, FL, United States: Chapman and Hall/CRC. doi: 10.1201/9781003256069-23

Mining significant pre-diabetes features of diabetes mellitus: a case study of Noakhali, Bangladesh

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

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, 491 LNICST, v-vi.

Preface

2023

Conference Publication

Convolutional neural network model to detect COVID-19 patients utilizing chest x-ray images

Satu, Md. Shahriare, Ahammed, Khair, Abedin, Mohammad Zoynul, Rahman, Md. Auhidur, Islam, Sheikh Mohammed Shariful, Azad, A. K. M., Alyami, Salem A. and Moni, Mohammad Ali (2023). Convolutional neural network model to detect COVID-19 patients utilizing chest x-ray images. First International Conference, MIET 2022, Noakhali, Bangladesh, 23-25 September 2022. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-34619-4_13

Convolutional neural network model to detect COVID-19 patients utilizing chest x-ray images

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

2023

Journal Article

Multi-disease detection using a prism-based surface plasmon resonance sensor: A TMM and FEM approach

Rumi, Rabeya Bosrin, Paul, Alok Kumar, Alyami, Salem A. and Moni, Mohammad Ali (2023). Multi-disease detection using a prism-based surface plasmon resonance sensor: A TMM and FEM approach. IEEE Transactions on NanoBioscience, 23 (1), 1-14. doi: 10.1109/tnb.2023.3286269

Multi-disease detection using a prism-based surface plasmon resonance sensor: A TMM and FEM approach

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

Journal Article

PSRTTCA: a new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning

Charoenkwan, Phasit, Pipattanaboon, Chonlatip, Nantasenamat, Chanin, Hasan, Md Mehedi, Moni, Mohammad Ali, Lio’, Pietro and Shoombuatong, Watshara (2023). PSRTTCA: a new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning. Computers in Biology and Medicine, 152 106368, 106368. doi: 10.1016/j.compbiomed.2022.106368

PSRTTCA: a new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning

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)

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

    Understanding the pathophysiology of stroke using bioinformatics and statistical genetics approaches

    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

    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

    Service extension and cost minimization in healthcare management of peripheral healthcare organizations in Bangladesh: analysis for service improvement

    Associate Advisor

    Other advisors: Associate Professor Asaduzzaman Khan

Enquiries

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