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Dr Mohammad Ali Moni

Honorary Senior Res Fellow

School of Health and Rehabilitation Sciences
Faculty of Health and Behavioural Sciences
m.moni@uq.edu.au

Overview

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.

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

Qualifications

  • Doctor of Philosophy, University of Cambridge

Publications

View all Publications

Supervision

  • Understanding the pathophysiology of stroke using bioinformatics and statistical genetics approaches

    Doctor Philosophy

  • Robust and Explainable AI to Solve Clinical Problems

    Doctor Philosophy

  • Advancing Maternal-Fetal Health in Underserved Communities: A Computer Vision Approach

    Master Philosophy

View all Supervision

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

View all Available Projects

Publications

Book

Book Chapter

  • A dynamic topic identification and labeling approach for COVID-19 tweets

    Shahriar, Khandaker Tayef, Islam, Muhammad Nazrul, Moni, Mohammad Ali and Sarker, Iqbal H. (2023). A dynamic topic identification and labeling approach for COVID-19 tweets. Applied Intelligence for Industry 4.0. (pp. 227-239) edited by Nazmul Siddique, Mohammad Shamsul Arefin, M. Shamim Kaiser and A.S.M. Kayes. New York, NY United States: CRC Press. doi: 10.1201/9781003256083-18

  • 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) Boca Raton: Chapman and Hall/CRC. doi: 10.1201/9781003256069-23

  • Methods for the analysis of micro-pollutants

    Ahmed, M. B., Johir, M. A.H., Ngo, Huu Hao, Guo, Wenshan, Zhou, J. L., Belhaj, D. and Moni, M. A. (2020). Methods for the analysis of micro-pollutants. Current Developments in Biotechnology and Bioengineering: Emerging Organic Micro-pollutants. (pp. 63-86) Amsterdam, Netherlands: Elsevier. doi: 10.1016/B978-0-12-819594-9.00004-8

Journal Article

Conference Publication

  • Observation of Heart Attack Patients Utilizing Machine Learning with Monarch Butterfly Optimization and IoT

    Rahman, Wahidur, Abul Ala Walid, Md., Saklain Galib, S. M., Rokhsana, Kaniz, Abdul Hai, Talha Bin, Mohammad Azad, Mir and Ali Moni, Mohammad (2023). Observation of Heart Attack Patients Utilizing Machine Learning with Monarch Butterfly Optimization and IoT. IEEE. doi: 10.1109/iccit60459.2023.10441444

  • CervixMed: Detecting cervical cancer based on combinational data using hybrid architecture

    Gupta, Debashis, Golder, Aditi, Haque, Md. Mahfuzul and Moni, Mohammad Ali (2023). CervixMed: Detecting cervical cancer based on combinational data using hybrid architecture. 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Port Macquarie, NSW Australia, 28 November - 1 December 2023. Piscataway, NJ United States: IEEE. doi: 10.1109/dicta60407.2023.00085

  • AI-Enhanced Biomedical Antennas for 2mm Brain Tumor Detection Using Scattering, Admittance and Impedance Parameters: A Comparative Analysis

    Hasan, Nafiul, Rana, Md. Masud, Hasan, Md Mahmudul and Moni, Mohammad Ali (2023). AI-Enhanced Biomedical Antennas for 2mm Brain Tumor Detection Using Scattering, Admittance and Impedance Parameters: A Comparative Analysis. IEEE. doi: 10.1109/icict4sd59951.2023.10303373

  • What baseline and intervention characteristics predict walking speed six months after stroke?

    Nayak, Neelam, Brauer, Sandra, Kuys, Suzanne, Moni, Mohammad Ali and Mahendran, Niruthikha (2023). What baseline and intervention characteristics predict walking speed six months after stroke?. Stroke 2023 – The Combined Stroke Society of Australasia and Smart Strokes Nursing and Allied Health Scientific Meeting, Melbourne, VIC, Australia, 22-25 August 2023. London, United Kingdom: Sage Publications.

  • Machine learning-based biomedical antenna for brain tumor detection

    Hasan, Nafiul, Aktar, Mousumi, Rana, Md. Masud and Moni, Mohammad Ali (2023). Machine learning-based biomedical antenna for brain tumor detection. International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM), Gazipur, Bangladesh, 16-17 June 2023. Piscataway, NJ, United States: IEEE. doi: 10.1109/ncim59001.2023.10212805

  • 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

  • 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

  • Predictive risk modelling in mental health issues using machine learning on graphs

    Lu, Haohui, Uddin, Shahadat, Hajati, Farshid, Khushi, Matloob and Moni, Mohammad Ali (2022). Predictive risk modelling in mental health issues using machine learning on graphs. ACSW 2022: Australasian Computer Science Week 2022, Online, 14 February 2022. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3511616.3513112

  • A machine learning model to recognise human emotions using electroencephalogram

    Roy, Nipa, Aktar, Sakifa, Ahamad, Md. Martuza and Moni, Mohammad Ali (2022). A machine learning model to recognise human emotions using electroencephalogram. 2021 5th International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh, 17-19 December 2021. Piscataway, NJ USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/EICT54103.2021.9733675

  • Early stage autism spectrum disorder detection of adults and toddlers using machine learning models

    Hasan, Minhazul, Ahamad, Md. Martuza, Aktar, Sakifa and Moni, Mohammad Ali (2022). Early stage autism spectrum disorder detection of adults and toddlers using machine learning models. 2021 5th International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh, 17-19 December 2021. Piscataway, NJ USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/EICT54103.2021.9733664

  • Early stage detection of heart failure using machine learning techniques

    Alom, Zulfikar, Azim, Mohammad Abdul, Aung, Zeyar, Khushi, Matloob, Car, Josip and Moni, Mohammad Ali (2022). Early stage detection of heart failure using machine learning techniques. International Conference on Big Data, IoT, and Machine Learning, Cox’s Bazar, Bangladesh, 23-25 September 2021. Singapore, Singapore: Springer Nature Singapore. doi: 10.1007/978-981-16-6636-0_7

  • Machine learning approaches to identify significant features for the diagnosis and prognosis of chronic kidney disease

    Mahbub, Nosin Ibna, Hasan, Md. Imran, Ahamad, Md. Martuza, Aktar, Sakifa and Moni, Mohammad Ali (2022). Machine learning approaches to identify significant features for the diagnosis and prognosis of chronic kidney disease. International Conference on Innovations in Science, Engineering and Technology (ICISET), Chittagong, Bangladesh, 26-27 February 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICISET54810.2022.9775827

  • SATLabel: a framework for sentiment and aspect terms based automatic topic labelling

    Shahriar, Khandaker Tayef, Moni, Mohammad Ali, Hoque, Mohammed Moshiul, Islam, Muhammad Nazrul and Sarker, Iqbal H. (2022). SATLabel: a framework for sentiment and aspect terms based automatic topic labelling. Machine Intelligence and Data Science Applications, Cumilla, Bangladesh, 26-27 December 2021. Gateway East, Singapore: Springer Nature Singapore. doi: 10.1007/978-981-19-2347-0_6

  • Towards explainable and privacy-preserving artificial intelligence for personalisation in autism spectrum disorder

    Mahmud, Mufti, Kaiser, M. Shamim, Rahman, Muhammad Arifur, Wadhera, Tanu, Brown, David J., Shopland, Nicholas, Burton, Andrew, Hughes-Roberts, Thomas, Mamun, Shamim Al, Ieracitano, Cosimo, Tania, Marzia Hoque, Moni, Mohammad Ali, Islam, Mohammed Shariful, Ray, Kanad and Hossain, M. Shahadat (2022). Towards explainable and privacy-preserving artificial intelligence for personalisation in autism spectrum disorder. 16th International Conference, UAHCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Virtual, 26 June - 1 July 2022. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-031-05039-8_26

  • Improved transfer learning architecture to classify Covid-19 affected chest X-rays using noisy student pre-training

    Fahim, Md Asif Iqbal, Naznin, Feroza, Moni, Mohammad Ali and Islam, Md Zahidul (2021). Improved transfer learning architecture to classify Covid-19 affected chest X-rays using noisy student pre-training. 2021 Joint 10th International Conference on Informatics, Electronics & Vision (ICIEV) and 2021 5th International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan, 16-20 August 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICIEVICIVPR52578.2021.9564125

  • Lung cancer prediction using curriculum learning based deep neural networks

    Zhou, Jackson, Khushi, Matloob, Moni, Mohammad Ali, Uddin, Shahadat and Poon, Simon K. (2021). Lung cancer prediction using curriculum learning based deep neural networks. 2021 IEEE International Conference on Digital Health (ICDH), Chicago, IL USA, 5-10 September 2021. Piscataway, NJ USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICDH52753.2021.00013

  • Movie genre classification with deep neural network using poster images

    Hossain, Nayeem, Ahamad, Md. Martuza, Aktar, Sakifa and Moni, Mohammad Ali (2021). Movie genre classification with deep neural network using poster images. 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), Dhaka, Bangladesh, 27-28 February 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/ICICT4SD50815.2021.9396778

  • Machine learning model to predict autism investigating eye-tracking dataset

    Akter, Tania, Ali, Mohammad Hanif, Khan, Md. Imran, Satu, Md. Shahriare and Moni, Mohammad Ali (2021). Machine learning model to predict autism investigating eye-tracking dataset. 2nd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2021, Dhaka, Bangladesh, 5-7 January 2021. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICREST51555.2021.9331152

  • The implementation of public health and economic measures during the first wave of COVID-19 by different countries with respect to time, infection rate and death rate

    Uddin, Shahadat, Imam, Tasadduq and Ali Moni, Mohammad (2021). The implementation of public health and economic measures during the first wave of COVID-19 by different countries with respect to time, infection rate and death rate. 2021 Australasian Computer Science Week Multiconference, Dunedin, New Zealand, 1-5 February 2021. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3437378.3437384

  • COVID-Hero: machine learning based COVID-19 awareness enhancement mobile game for children

    Satu, Md. Shahriare, Mizan, K. Shayekh Ebne, Jerin, Syeda Anika, Whaiduzzaman, Md, Barros, Alistair, Ahmed, Kawsar and Moni, Mohammad Ali (2021). COVID-Hero: machine learning based COVID-19 awareness enhancement mobile game for children. First International Conference on Applied Intelligence and Informatics, AII 2021, Online, 30-31 July 2021. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-82269-9_25

  • Improved Machine Learning based Classification Model for Early Autism Detection

    Akter, Tania, Khan, Md. Imran, Ali, Mohammad Hanif, Satu, Md. Shahriare, Uddin, Md. Jamal and Moni, Mohammad Ali (2021). Improved Machine Learning based Classification Model for Early Autism Detection. doi: 10.1109/ICREST51555.2021.9331013

  • Machine learning and bioinformatics models to identify gene expression patterns of glioblastoma associated with disease progression and mortality

    Choudhury, Zakia Zinat, Chowdhury, Utpala Nanda, Ahmad, Shamim, Islam, M. Babul, Quinn, Julian M.W. and Moni, Mohammad Ali (2021). Machine learning and bioinformatics models to identify gene expression patterns of glioblastoma associated with disease progression and mortality. International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering, Rajshahi, Bangladesh, 26-27 December 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/IC4ME253898.2021.9768525

  • Survival prediction for prostate cancer using machine learning and bioinformatics models

    Chowdhury, Utpala Nanda, Ahmad, Shamim, Islam, M. Babul and Moni, Mohammad Ali (2021). Survival prediction for prostate cancer using machine learning and bioinformatics models. 2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), Rajshahi, Bangladesh, 26-27 December 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/IC4ME253898.2021.9768443

  • Drug compound prediction-based analysis of cigarette smoking to Pancreatic Cancer patients: a bioinformatics study

    Taz, Tasnimul Alam, Kawsar, Md, Siddique, Sinthia, Ahmed, Kawsar, Moni, Mohammad Ali and Paul, Bikash Kumar (2020). Drug compound prediction-based analysis of cigarette smoking to Pancreatic Cancer patients: a bioinformatics study. IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), Bhubaneswar, India, 26-27 December 2020. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/WIECON-ECE52138.2020.9397979

  • Bioinformatics approach to analyze gene expression profile and comorbidities of gastric cancer

    Datta, Ratri, Podder, Nitun Kumar, Rana, Humayan Kabir, Islam, Md Khaled Ben and Moni, Mohammad Ali (2020). Bioinformatics approach to analyze gene expression profile and comorbidities of gastric cancer. Institute of Electrical and Electronics Engineers Inc.. doi: 10.1109/ICCIT51783.2020.9392587

  • Identifying the stability of couple relationship applying different machine learning techniques

    Satu, Md. Shahriare, Howlader, K. C., Hosen, Md Parvej, Chowdhury, Noton and Moni, Mohammad Ali (2020). Identifying the stability of couple relationship applying different machine learning techniques. Institute of Electrical and Electronics Engineers Inc.. doi: 10.1109/ICECE51571.2020.9393131

  • Comorbidity effects of mitochondrial dysfunction to the progression of neurological disorders: Insights from a systems biomedicine perspective

    Satu, Md. Shahriare, Chandra Howlader, Koushik, Niamat Ullah Akhund, Tajim Md., Quinn, Julian M.W., Lio, Pietro and Moni, Mohammad Ali (2019). Comorbidity effects of mitochondrial dysfunction to the progression of neurological disorders: Insights from a systems biomedicine perspective. 2019 22nd International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 18-20 December 2019. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICCIT48885.2019.9038388

  • A Bayesian optimization framework for the prediction of diabetes mellitus

    Rahman, Md. Abdur, Shoaib, S. M., Amin, Md. Al, Toma, Rafia Nishat, Moni, Mohammad Ali and Awal, Md. Abdul (2019). A Bayesian optimization framework for the prediction of diabetes mellitus. 2019 5th International Conference on Advances in Electrical Engineering (ICAEE), Dhaka, Bangladesh, 26-28 September 2019. Piscataway, NJ USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICAEE48663.2019.8975480

  • Identification of genetic association of thyroid cancer with Parkinsons disease, osteoporosis, chronic heart failure, chronic kidney disease, type 1 diabetes and type 2 diabetes

    Hossain, Md. Ali, Asa, Tania Akter, Saiful Islam, Sheikh Muhammad, Hussain, Muhammad Sajjad and Moni, Mohammad Ali (2019). Identification of genetic association of thyroid cancer with Parkinsons disease, osteoporosis, chronic heart failure, chronic kidney disease, type 1 diabetes and type 2 diabetes. 2019 5th International Conference on Advances in Electrical Engineering (ICAEE), Dhaka, Bangladesh, 26 - 28 September 2019. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICAEE48663.2019.8975560

  • A Systems Biology Approach to Identifying Genetic Markers that Link Progression of Parkinson's Disease to Risk Factors related to Ageing, Lifestyle and Type 2 Diabetes

    Sakib, Najmus, Chowdhury, Utpala Nanda, Islam, M. Babul, Huq, Fazlul, Quinn, Julian M.W. and Moni, Mohammad Ali (2019). A Systems Biology Approach to Identifying Genetic Markers that Link Progression of Parkinson's Disease to Risk Factors related to Ageing, Lifestyle and Type 2 Diabetes. 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), Rajshahi, Bangladesh, 11-12 July 2019. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/IC4ME247184.2019.9036535

  • Delineating Common Cell Pathways that Influence Type 2 Diabetes and Neurodegenerative Diseases using a Network-based Approach

    Chowdhury, Utpala Nanda, Hasan, Md. Al Mehedi, Ahmad, Shamim, Islam, M. Babul, Quinn, Julian M.W. and Moni, Mohammad Ali (2019). Delineating Common Cell Pathways that Influence Type 2 Diabetes and Neurodegenerative Diseases using a Network-based Approach. 5th International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering, IC4ME2 2019, Rajshahi, Bangladesh, 11-12 July 2019. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/IC4ME247184.2019.9036525

  • Network-based quantitative frameworks to identify pleotropic factors that influence for cardiomyopathy progression

    Haidar, Md. Nasim, Islam, M. Babul, Chowdhury, Utpala Nanda, Huq, Fazlul, Quinn, Julian M.W. and Moni, Mohammad Ali (2019). Network-based quantitative frameworks to identify pleotropic factors that influence for cardiomyopathy progression. 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), Rajshahi, Bangladesh, 11-12 July 2019. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/IC4ME247184.2019.9036486

  • SVM model for feature selection to increase accuracy and reduce false positive rate in falls detection

    Rashed-Al-Mahfuz, Md, Hoque, Md. Robiul, Pramanik, Bimal Kumar, Hamid, Md. Ekramul and Moni, Mohammad Ali (2019). SVM model for feature selection to increase accuracy and reduce false positive rate in falls detection. 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), Rajshahi, Bangladesh, 11 - 12 July 2019. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/IC4ME247184.2019.9036529

  • Early detection of autism by extracting features: a case study in Bangladesh

    Satu, Md. Shahriare, Farida Sathi, Farha, Arifen, Md. Sadrul, Hanif Ali, Md. and Moni, Mohammad Ali (2019). Early detection of autism by extracting features: a case study in Bangladesh. 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh, 10 - 12 January 2019. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICREST.2019.8644357

  • Detecting depression using K-nearest neighbors (KNN) classification technique

    Islam, Md Rafiqul, Kamal, Abu Raihan M., Sultana, Naznin, Islam, Robiul, Moni, Mohammad Ali and Ulhaq, Anwaar (2018). Detecting depression using K-nearest neighbors (KNN) classification technique. 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshahi, Bangladesh, 8 - 9 February 2018. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/IC4ME2.2018.8465641

  • Comparing viral (HIV) and bacterial (staphylococcus aureus) infection of the bone tissue

    Moni, Mohammad Ali, Liò, Pietro and Milanesi, Luciano (2013). Comparing viral (HIV) and bacterial (staphylococcus aureus) infection of the bone tissue. BIOINFORMATICS 2013 - International Conference on Bioinformatics Models, Methods and Algorithms, Barcelona, Spain, 11-14 February 2013. Setúbal, Portugal: SciTePress.

PhD and MPhil Supervision

Current Supervision

  • Understanding the pathophysiology of stroke using bioinformatics and statistical genetics approaches

    Doctor Philosophy — Principal Advisor

    Other advisors:

  • Robust and Explainable AI to Solve Clinical Problems

    Doctor Philosophy — Principal Advisor

    Other advisors:

  • Advancing Maternal-Fetal Health in Underserved Communities: A Computer Vision Approach

    Master Philosophy — Principal Advisor

  • Developing AI-based Discission Support System utilising multimodal data

    Doctor Philosophy — Principal Advisor

    Other advisors:

  • Coloured noise estimation using electroencephalogram data and deep-learning method for improvement of cognitive function

    Doctor Philosophy — Principal Advisor

    Other advisors:

  • Wearable devices and AI Models for Monitoring, Predicting and Assessment Post-stroke Recovery

    Doctor Philosophy — Principal Advisor

    Other advisors:

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

    Doctor Philosophy — Associate Advisor

    Other advisors:

  • Leisure Activities and Mental Health of Australian Adolescents

    Doctor Philosophy — Associate Advisor

    Other advisors:

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • 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

For Media assistance please contact The Office of Marketing and Communications, (07) 3365 1120

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ORCID: 0000-0003-0756-1006

Scopus ID: 35119094400

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