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  • Dr Syed Afaq Ali Shah

    Dr. Syed Afaq Shah is a Senior Lecturer (Computer Science) in the School of Science and core member of Centre for AI and Machine Learning, ECU.

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Dr Syed Afaq Ali Shah

Senior Lecturer

Staff Member Details
Telephone: +61 8 6304 2379
Email: afaq.shah@ecu.edu.au
Campus: Joondalup  
ORCID iD: https://orcid.org/0000-0003-2181-8445

Dr. Syed Afaq Ali Shah is a Senior Lecturer in the School of Science and a member of the Centre for AI and Machine Learning.

Current Teaching

  • CSI6208 - Programming Principles (Postgraduate)
  • CSP2151 - Programming Fundamentals

Background

Dr. Afaq Shah is a Senior Lecturer at Edith Cowan University (ECU), Perth. He leads the Robotics and Artificial Intelligence Research (RAIR) Group, advancing interdisciplinary research in artificial intelligence, robotics, computer vision, and data-driven technologies. He actively seeks and recruits high-quality PhD candidates to undertake research in the fields of Artificial Intelligence, Machine Learning, and related areas.

Before joining ECU, Dr. Shah served as a Lecturer at Murdoch University (2018-2021) and as a Lecturer in Information and Communication Technology at Central Queensland University in 2018. He earned his PhD from The University of Western Australia, where he subsequently worked as a Research Fellow for two and a half years. Prior to commencing his academic career, he spent seven years in the aviation industry, progressing from Project Manager to Deputy Chief Engineer (Information Technology), where he led the development and implementation of large-scale technology initiatives.

Dr. Shah's research focuses on Artificial Intelligence, Deep Learning, Agentic AI, Computational Empathy for AI systems, Computer Vision, and Health Analytics. His work involves developing advanced AI techniques for image, video, and multimodal data analysis, with applications spanning healthcare, agriculture, remote sensing, security, surveillance, and intelligent monitoring systems. His research has contributed significantly to areas including machine learning, 3D feature descriptors, object recognition and reconstruction, image segmentation, biometrics, 2D and 3D scene understanding, human-computer interaction, action and gesture recognition, image captioning, and predictive health analytics, particularly for cardiovascular and Alzheimer's disease detection.

He has authored and co-authored more than 80 peer-reviewed publications in leading journals and premier international conferences, including the International Journal of Computer Vision (IJCV), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Pattern Recognition, NeurIPS, and ECCV. He is also co-author of the book ‘A Guide to Convolutional Neural Networks for Computer Vision’, which provides a comprehensive overview of modern deep learning techniques for visual intelligence.

Dr. Shah has secured more than AUD $350,000 in competitive research funding and actively collaborates with academic, industry, and government partners on applied AI research. He is a Certified Professional of the Australian Computer Society (ACS) and a Fellow of the Higher Education Academy (FHEA), United Kingdom, reflecting his commitment to both professional excellence and high-quality teaching and learning.

Professional Memberships

  • 2019-Present – Australian Computer Society Member
  • 2020-Present – Fellow Higher Education Academy (FHEA), UK
  • Life Member of Pakistan Engineering Council (Member of Washington Accord)

Awards and Recognition

National and International Awards

  • 2020 – DST Best Contribution to Science Award, Digital Image Computing: Techniques and Applications (DICTA) 2020
  • 2020 - ACU Early Career Conference Award
  • 2019 - Australian Computer Society Certified Professional
  • 2018 - NeurIPS Conference Travel Award
  • 2017 - UWA Research Collaboration Award
  • 2016 - UWA Start Something Award for Research Impact through Enterprise

University and National Teaching Awards

  • 2026 - 2026 FIT Dean’s Awards for Equity, Diversity and Inclusion (In collaboration with Monash)
  • 2020 - Fellow Higher Education Academy (FHEA), UK
  • 2019 – Associate Fellow, Higher Education Academy (FHEA), UK
  • 2016 - Nominated for Teaching and Learning Excellence Award (UWA)

Media

National and International Research Positions

  • 2025 – General Chair, Australasian Conference on Robotics and Automation (ACRA)
  • 2024 - IEEE , Digital Image Computing: Techniques and Applications (DICTA) Workshop Chair
  • 2023 – IEEE , Digital Image Computing: Techniques and Applications (DICTA) Awards Chair
  • 2022 - Australasian Joint Conference on Artificial Intelligence, Workshop Chair
  • 2021-Present – Associate Editor, Network: Computation in Neural Systems
  • 2021 – Workshop and Special Session Chair, DICTA2021
  • 2020 – Guest Editor, Remote Sensing Journal
  • 2020-Present – Lead Reviewer Serbian Research Fund and reviewer, Mitacs Accelerate grant (Canada).
  • 2017-2018 – Program committee member Advanced Concepts for Intelligent Vision Systems (ACIVS).

Research Areas and Interests

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning
  • Agentic AI
  • Generative AI
  • Large Language Models
  • Pattern recognition
  • Computer Vision
  • Health Analytics
  • Facial Analysis
  • Robotics and Autonomous Systems
  • Object Detection, Recognition and Segmentation
  • Image/video Processing
  • Digital AgriTech

Qualifications

  • Doctor of Philosophy, The University of Western Australia, 2016.

Research Outputs

2026

Journal Articles

  • Tahir, S., Abu-Khalaf, J., Johnson, J., Shah, A. (2026). Semantic Context Improvisational Retrieval-Augmented Generation for Empathic Conversational AI. Neural Computing and Applications, 1(Accepted/In press), 15.
  • Narasapuram, NK., Shah, A., Shiratuddin, MF., Sohel, F. (2026). Explainable artificial intelligence models for detecting suspicious bank transactions. International Journal of Machine Learning and Cybernetics, 17(3), article number 111. https://doi.org/10.1007/s13042-026-02994-w.

Conference Publications

  • Arora, C., Arachchige, A., Janjua, N., Shah, A. (2026). GenEDIt. GenEDIt: A Context-Aware Equity, Diversity and Inclusion Principles Integration Tool for Software Engineering Education (6). IEEE/ACM.
  • Mirnateghi, N., Tahir, S., Islam, S., Barnby, J., Shah, A. (2026). Lezak: A general purpose neuropsychological approach to explain complex neural networks. . (8). INN Society.

2025

Journal Articles

  • Xion, G., Wang, N., Li, H., Zhu, G., Shah, A., Zhang, L., Bennamoun, M. (2025). Enhancing Object recognition: The Role of Object Knowledge Decomposition and Component-Labeled Datasets. Neurocomputing, 617(2025), article number 128969. https://doi.org/10.1016/j.neucom.2024.128969.

Conference Publications

  • Khodkar, Z., Yan, S., Shah, A., Biswa, R., De Souza Junior, P., Uggalla, LC., Ng, DW. (2025). CubeSat Downlink Communications Enhanced by Movable Antennas. 2025 IEEE International Conference on Communications (1061-1066). IEEE. https://doi.org/10.1109/ICC52391.2025.11161675.
  • Mirnateghi, N., Tahir, S., Islam, S., Shah, A. (2025). M-AIDE: Mechanistic Agentic Interpretability for Decoding Empathy in Language Models. Proceedings of the 40th Conference on Image and Vision Computing New Zealand (IVCNZ) (255-260). IEEE. https://doi.org/10.1109/IVCNZ67716.2025.11281845.
  • Kwok, WH., Shah, A., Wang, G. (2025). Identifying Key Factors Associated with Assistive Technology Availability for Dementia Care Using Machine Learning. Intelligent Health Systems – From Technology to Data and Knowledge (1242-1243). IOS Press BV. https://doi.org/10.3233/SHTI250592.
  • Wang, Q., Xu, Z., Lin, Y., Ye, J., Li, H., Zhu, G., Shah, A., Bennamoun, M., Zhang, L. (2025). DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action Recognition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (55-72). Springer Science. https://doi.org/10.1007/978-3-031-72907-2_4.
  • Tahir, S., Abu-Khalaf, J., Shah, A., Johnson, J. (2025). Therapying Outside the Box: Innovating the Implementation and Evaulation of CBT in Therapeutic Artificial Agents. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (203-213). Springer Science. https://doi.org/10.1007/978-981-96-0573-6_15.
  • Shah, A. (2025). Beyond Deep Learning: Agentic AI Framework for Object Detection. 2025 40th International Conference on Image and Vision Computing New Zealand (IVCNZ) (347-352). IEEE. https://doi.org/10.1109/IVCNZ67716.2025.11281644.

2024

Journal Articles

  • Bughio, S., Cook, D., Shah, A. (2024). Investigating the intersections of vulnerability detection and Internet of Medical Things (IoMT) in healthcare, a scoping review protocol for Remote Patient Monitoring. Research, Society and Development, 13(6), Article number e11313646080. https://doi.org/10.33448/rsd-v13i6.46080.
  • Śliwiak, P., Shah, A. (2024). Text-to-Text Generative Approach for Enhanced Complex Word Identification. Neurocomputing, 610(2024), article number 128501. https://doi.org/10.1016/j.neucom.2024.128501.
  • Lei, Z., Zhu, W., Liu, J., Hua, C., Li, J., Shah, A., Zhang, L., Bennamoun, M., Mao, C. (2024). RLAD: A Reliable Hippo-guided Multi-task Model for Alzheimer's Disease Diagnosis. IEEE Journal of Biomedical and Health Informatics, 2024(Article in press), 1-12. https://doi.org/10.1109/JBHI.2024.3412926.
  • Bughio, K., Cook, D., Shah, A. (2024). Developing a Novel Ontology for Cybersecurity in Internet of Medical Things-Enabled Remote Patient Monitoring. Sensors, 24(9), Article number 2804. https://doi.org/10.3390/s24092804.
  • Li, H., Zhu, G., Zhang, L., Jiang, Y., Dang, Y., Hau, H., Shen, P., Zhao, X., Shah, A., Bennamoun, M. (2024). Scene Graph Generation: A comprehensive survey. Neurocomputing, 566(2024), article number 127052. https://doi.org/10.1016/j.neucom.2023.127052.

Conference Publications

  • Mirnateghi, N., Islam, S., Shah, A. (2024). Towards Explainability of Affordance Learning in Robot Vision. IEEE Digital Image Computing: Techniques & Applications (545-552). IEEE. https://doi.org/10.1109/DICTA63115.2024.00085.
  • Tahir, S., Mirnateghi, N., Shah, A., Sohel, F. (2024). DEER: Deep Emotion-Sets for Fine-Grained Emotion Recognition. IEEE Digital Image Computing: Techniques & Applications (6 pages). IEEE. https://doi.org/10.1109/DICTA63115.2024.00034.
  • Wang, N., Zhu, G., Li, H., Zhang, L., Shah, A., Bennamoun, M. (2024). Language Model Guided Interpretable Video Action Reasoning. CVPR 2024 conference (10 pages). IEEE. https://doi.org/10.1109/CVPR52733.2024.01786.
  • Bughio, K., Cook, D., Shah, A. (2024). Novel Knowledge Graph-Based Modeling for Vulnerability Detection in the Internet of Medical Things. Recent Challenges in Intelligent Information and Database Systems, Communications in Computer and Information Science (314-325). SPRINGER. https://doi.org/10.1007/978-981-97-5937-8_26.
  • Bughio, S., Cook, D., Shah, A. (2024). GenAI in Rule-based Systems for IoMT Security: Testing and Evaluation. Procedia Computer Science (5330 - 5339). Elsevier B.V.. https://doi.org/10.1016/j.procs.2024.09.652.

2023

Journal Articles

  • Li, J., Zhu, G., Hua, C., Feng, M., Bennamoun, B., Li, P., Lu, X., Song, J., Shen, P., Xu, X., Mei, L., Zhang, L., Shah, A., Bennamoun, M. (2023). A Systematic Collection of Medical Image Datasets for Deep Learning. ACM Computing Surveys, 56(5), Article number 116. https://doi.org/10.1145/3615862.
  • Ye, G., Song, J., Feng, M., Zhu, G., Shen, P., Zhang, L., Shah, A., Bennamoun, M. (2023). Position and structure-aware graph learning. Neurocomputing, 556(November), Article number 126581. https://doi.org/10.1016/j.neucom.2023.126581.
  • Shah, A., Deng, W., Cheema, M., Bais, A. (2023). CommuNety: deep learning-based face recognition system for the prediction of cohesive communities. Multimedia Tools and Applications, 82(7), 10641-10659. https://doi.org/10.1007/s11042-022-13741-y.

Conference Publications

  • Khalifa, Z., Shah, A. (2023). A Large Scale Multi-view RGBD Visual Affordance Learning Dataset. Proc of the 30th IEEE International Conference on Image Processing (ICIP 2023) (5). IEEE. https://doi.org/10.48550/arXiv.2203.14092.
  • Mahmood, H., Iqbal, A., Islam, S., Shah, A. (2023). 3D brain registration with intensity shift robustness. Proc of the 30th IEEE International Conference on Image Processing (ICIP 2023) (2805-2809). IEEE. https://doi.org/10.1109/ICIP49359.2023.10222341.
  • Islam, S., Shah, A., Nguyen, CD. (2023). Deep Learning Approach for Automatic Segmentation of Dirt on Cattle Skin using Image Data. International Conference Image and Vision Computing New Zealand (6 pages). IEEE Computer Society. https://doi.org/10.1109/IVCNZ61134.2023.10344224.
  • Mirnateghi, N., Shah, A., Bennamoun, M. (2023). Deep Bayesian Image Set Classification Approach for Defence against Adversarial Attacks. 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (501-508). IEEE. https://doi.org/10.1109/DICTA60407.2023.00075.
  • Gu, J., Jiang, M., Li, H., Lu, X., Zhu, G., Shah, A., Zhang, L., Bennamoun, M. (2023). UE4-NeRF: Neural Radiance Field for Real-Time Rendering of Large-Scale Scene. The Thirty-seventh Annual Conference on Neural Information Processing Systems (13 pages). NeurIPS. https://ro.ecu.edu.au/ecuworks2022-2026/3728.
  • Shah, A., Khalifa, Z. (2023). Hierarchical Transformer for Visual Affordance Understanding using a Large Scale Dataset. IEEE/RSJ International Conference on Intelligent Robots and Systems (11371-11376). IEEE. https://doi.org/10.1109/IROS55552.2023.10341976.

2022

Journal Articles

  • Zhang, L., Li, J., Lu, G., Shen, P., Bennamoun, M., Shah, A., Miao, Q., Zhu, G., Li, P., Lu, X. (2022). Analysis and Variants of Broad Learning System. IEEE Transactions on Systems, Man and Cybernetics: Systems, 52(1), 334 - 344. https://doi.org/10.1109/TSMC.2020.2995205.
  • Ayris, D., Imtiaz, M., Horbury, K., Williams, B., Blackney, M., See, C., Shah, A. (2022). Novel Deep Learning Approach to Model and Predict the spread of COVID-19. Intelligent Systems with Applications, 14(May 2022), article number 200068. https://doi.org/10.1016/j.iswa.2022.200068.
  • Shah, A., Pickupana, P., Luo, H., Ekeze, A., Sohel, F., Laga, H., Li, C., Paynter, B., Wang, P. (2022). Automatic and Fast Classification of Barley Grains from Images: A Deep Learning Approach. Smart Agricultural Technology, 2(December 2022), article number 100036. https://doi.org/10.1016/j.atech.2022.100036.
  • Zeng, Z., Wang, T., Ma, F., Zhang, L., Shen, P., Shah, A., Bennamoun, M. (2022). Probability-based Framework to Fuse Temporal Consistency and Semantic Information for Background Segmentation. IEEE Transactions on Multimedia, 24(2022), 740-754. https://doi.org/10.1109/TMM.2021.3058770.
  • Imtiaz, M., Shah, A., Ur Rehman, Z. (2022). A review of arthritis diagnosis techniques in artificial intelligence era: Current trends and research challenges. Neuroscience Informatics, 2(4), Article number 100079. https://doi.org/10.1016/j.neuri.2022.100079.

2021

Journal Articles

  • Nadeem, U., Shah, A., Bennamoun, M., Togneri, R., Sohel, F. (2021). Real time surveillance for low resolution and limited data scenarios: An image set classification approach. Information Sciences, 580(November 2021), 578-597. https://doi.org/10.1016/j.ins.2021.08.093.
  • Li, P., Kong, X., Li, J., Zhu, G., Lu, X., Shen, P., Shah, A., Bennamoun, M., Hua, T. (2021). A Dataset of Pulmonary Lesions With Multiple-Level Attributes and Fine Contours. Academy of Nutrition and Dietetics, 2(February 2021), Article number 609349. https://doi.org/10.3389/fdgth.2020.609349.
  • Fan, Z., Li, J., Zhang, L., Zhu, G., Li, P., Lu, X., Shen, P., Shah, A., Bennamoun, M., Hua, T., Wei, W. (2021). U-net based analysis of MRI for Alzheimer’s disease diagnosis. Neural Computing and Applications, 33(20), 13587-13599. https://doi.org/10.1007/s00521-021-05983-y.
  • Xue, Z., Li, P., Zhang, L., Lu, X., Zhu, G., Shen, P., Shah, A., Bennamoun, M. (2021). Multi-Modal Co-Learning for Liver Lesion Segmentation on PET-CT Images. IEEE Transactions on Medical Imaging, 40(12), 3531-3542. https://doi.org/10.1109/TMI.2021.3089702.
  • Lubna, ., Mufti, N., Shah, A. (2021). Automatic number plate recognition:A detailed survey of relevant algorithms. Sensors, 21(9), Article number 3028. https://doi.org/10.3390/s21093028.

Conference Publications

  • Sharif, N., Bennamoun, M., Liu, W., Shah, A. (2021). SubICap: Towards Subword-informed Image Captioning. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (Article number 20696987). IEEE. https://doi.org/10.1109/WACV48630.2021.00358.

2020

Book Chapters

  • Sharif, N., Nadeem, U., Shah, A., Bennamoun, M., Liu, W. (2020). Vision to Language: Methods, Metrics and Datasets. Machine Learning Paradigms. Advances in Deep Learning-based Technological Applications (9-62). Springer. https://doi.org/10.1007/978-3-030-49724-8_2.

Journal Articles

  • Zhu, G., Zhang, L., Li, H., Shen, P., Shah, A., Bennamoun, M. (2020). Topology-learnable graph convolution for skeleton-based action recognition. Pattern Recognition Letters, 135(Jul 2020), 286-292. https://doi.org/10.1016/j.patrec.2020.05.005.
  • Zhu, G., Zhang, L., Yang, L., Mei, L., Shah, A., Bennamoun, M., Shen, P. (2020). Redundancy and Attention in Convolutional LSTM for Gesture Recognition. IEEE Transactions on Neural Networks and Learning Systems, 31(4), 1323-1335. https://doi.org/10.1109/TNNLS.2019.2919764.
  • Li, H., Zhang, L., Zhang, X., Zhang, M., Zhu, G., Shen, P., Li, P., Bennamoun, M., Shah, A. (2020). Color vision deficiency datasets & recoloring evaluation using GANs. Multimedia Tools and Applications, 79(37-38), 27583-27614. https://doi.org/10.1007/s11042-020-09299-2.
  • Zhang, L., Zhang, J., Shen, P., Zhu, G., Li, P., Lu, X., Zhang, H., Shah, A., Bennamoun, M. (2020). Block Level Skip Connections across Cascaded V-Net for Multi-Organ Segmentation. IEEE Transactions on Medical Imaging, 39(9), 2782-2793. https://doi.org/10.1109/TMI.2020.2975347.
  • Chen, Y., Sohel, F., Shah, A., Ding, S. (2020). Deep Boltzmann machine for corrosion classification using eddy current pulsed thermography. Optik, 219(Oct 2021), Article number 164828. https://doi.org/10.1016/j.ijleo.2020.164828.

Conference Publications

  • Zhang, L., Wang, X., Li, H., Zhu, G., Shen, P., Li, P., Lu, X., Shah, A., Bennamoun, M. (2020). Structure-Feature based Graph Self-adaptive Pooling. The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020 (3098-3104). Association for Computing Machinery, Inc. https://doi.org/10.1145/3366423.3380083.
  • Shah, A., Bougre, M., Akhtar, N., Bennamoun, M., Zhang, L. (2020). Efficient Detection of Pixel-Level Adversarial Attacks. Proceedings of International Conference on Image Processing, ICIP (718-722). IEEE Computer Society. https://doi.org/10.1109/ICIP40778.2020.9191084.
  • Sharif, N., White, L., Bennamoun, M., Liu, W., Shah, A. (2020). WEmbSim: A Simple yet Effective Metric for Image Captioning. Proceedings of the 2020 Digital Image Computing: Techniques and Applications (DICTA) Conference (Article number 9363392). Institute of Electrical and Electronics Engineers Inc. (IEEE). https://doi.org/10.1109/DICTA51227.2020.9363392.
  • Sharif, N., Jalwana, M., Bennamoun, M., Liu, W., Shah, A. (2020). Leveraging Linguistically-aware Object Relations and NASNet for Image Captioning. Proceedings of 35th International Conference on Image and Vision Computing New Zealand (Article number 9290719). IEEE. https://doi.org/10.1109/IVCNZ51579.2020.9290719.
  • Zhang, L., Liu, Y., Xiao, H., Yang, L., Zhu, G., Shah, A., Bennamoun, M., Shen, P. (2020). Efficient Scene Text Detection with Textual Attention Tower. Proceedings of International Conference on Acoustics, Speech and Signal Processing (4272-4276). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP40776.2020.9054213.
  • Shah, A. (2020). Spatial Hierarchical Analysis Deep Neural Network for RGB-D Object Recognition. Image and Video Technology PSIVT 2019 International Workshops Sydney, NSW, Australia, November 18–22, 2019 Revised Selected Papers (183-193). Springer. https://doi.org/10.1007/978-3-030-39770-8_15.

2019

Book Chapters

Journal Articles

  • Zhu, G., Zhang, L., Shen, P., Song, J., Shah, A., Bennamoun, M. (2019). Continuous gesture segmentation and recognition using 3dcnn and convolutional lstm. IEEE Transactions on Multimedia, 21(4), 1011-1021. https://doi.org/10.1109/TMM.2018.2869278.
  • Sharif, N., White, L., Bennamoun, M., Liu, W., Shah, A. (2019). LCEval: Learned Composite Metric for Caption Evaluation. International Journal of Computer Vision, 127(10), 1586-1610. https://doi.org/10.1007/s11263-019-01206-z.
  • Shah, A., Bennamoun, M., Molton, M. (2019). Machine learning approaches for prediction of facial rejuvenation using real and synthetic data. IEEE Access, 7(2019), 23779-23787. https://doi.org/10.1109/ACCESS.2019.2899379.

Conference Publications

  • Zhang, L., Zhang, S., Shen, P., Zhu, G., Shah, A., Bennamoun, M. (2019). Relationship detection based on object semantic inference and attention mechanisms. ICMR 2019 - Proceedings of the 2019 ACM International Conference on Multimedia Retrieval (68-72). Association for Computing Machinery, Inc. https://doi.org/10.1145/3323873.3325025.
  • Shah, A., Bennamoun, M., Molton, M. (2019). A Fully Automatic Framework for Prediction of 3D Facial Rejuvenation. 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ 2018) (Article number 8634657). IEEE. https://doi.org/10.1109/IVCNZ.2018.8634657.
  • Shah, A., Bennamoun, M., Molton, M. (2019). A Training-Free Mesh Upsampling and Morphing Technique for 3D Face Rejuvenation. 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ 2018) (Article number 8634685). IEEE. https://doi.org/10.1109/IVCNZ.2018.8634685.

2018

Journal Articles

  • Zhang, L., Feng, Y., Shen, P., Zhu, G., Wei, W., Song, J., Shah, A., Bennamoun, M. (2018). Efficient finer-grained incremental processing with MapReduce for big data. Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications, 80(March 2018), 102-111. https://doi.org/10.1016/j.future.2017.09.079.
  • Zhang, L., Xu, Q., Zhu, G., Song, J., Zhang, X., Shen, P., Wei, W., Shah, A., Bennamoun, M. (2018). Improved colour-to-grey method using image segmentation and colour difference model for colour vision deficiency. IET Image Processing, 12(3), 314-319. https://doi.org/10.1049/iet-ipr.2017.0482.
  • Zhang, L., Li, H., Shen, P., Zhu, G., Song, J., Shah, A., Bennamoun, M., Zhang, L. (2018). Improving Semantic Image Segmentation with a Probabilistic Superpixel-Based Dense Conditional Random Field. IEEE Access, 6(2018), 15297-15310. https://doi.org/10.1109/ACCESS.2018.2814568.
  • Shah, A., Bennamoun, M., Boussaid, F., While, L. (2018). Evolutionary Feature Learning for 3-D Object Recognition. IEEE Access, 6(2018), 2434-2444. https://doi.org/10.1109/ACCESS.2017.2783331.
  • Zhang, L., Wang, L., Zhang, X., Shen, P., Bennamoun, M., Zhu, G., Shah, A., Song, J. (2018). Semantic scene completion with dense CRF from a single depth image. Neurocomputing, 318(27-Nov-18), 182-195. https://doi.org/10.1016/j.neucom.2018.08.052.

Conference Publications

  • Sharif, N., White, L., Bennamoun, M., Shah, A. (2018). Learning-based composite metrics for improved caption evaluation. Proceedings of ACL 2018, Student Research Workshop (14-20). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p18-3003.
  • Zhang, L., Zhu, G., Mei, L., Shen, P., Shah, A., Bennamoun, M. (2018). Attention in convolutional LSTM for gesture recognition. Proceedings of 32nd Conference on Neural Information Processing Systems (NeurIPS 2018) (1953-1962). Neural information processing systems foundation.
  • Zhang, L., Kong, X., Shen, P., Zhu, G., Song, J., Shah, A., Bennamoun, M. (2018). Reflective Field for Pixel-Level Tasks. Proceedings - International Conference on Pattern Recognition (529-534). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2018.8545817.
  • Sharif, N., White, L., Bennamoun, M., Shah, A. (2018). NNEval: Neural network based evaluation metric for image captioning. Proceedings of Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (39-55). Springer Verlag. https://doi.org/10.1007/978-3-030-01237-3_3.

2017

Journal Articles

Conference Publications

  • Zhang, L., Zhu, G., Shen, P., Song, J., Shah, A., Bennamoun, M. (2017). Learning spatiotemporal features using 3DCNN and convolutional LSTM for gesture recognition. Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017 (3120-3128). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCVW.2017.369.
  • Shah, A., Nadeem, U., Bennamoun, M., Sohel, F., Togneri, R. (2017). Efficient Image Set Classification Using Linear Regression Based Image Reconstruction. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (601-610). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2017.88.
  • Hu, H., Shah, A., Bennamoun, M., Molton, M. (2017). 2D and 3D face recognition using convolutional neural network. IEEE Region 10 Annual International Conference, Proceedings/TENCON (133-138). IEEE. https://doi.org/10.1109/TENCON.2017.8227850.

2016

Journal Articles

  • Molton, M., Shah, A., Bennamoun, M. (2016). Improving the Face of Cosmetic Medicine: An Automatic Three-dimensional Analysis System for Facial Rejuvenation. Journal of Aesthetic & Reconstructive Surgery, 2(2), 6p.. https://doi.org/10.4172/2472-1905.100021.
  • Shah, A., Bennamoun, M., Boussaid, F. (2016). Iterative deep learning for image set based face and object recognition. Neurocomputing, 174(22-Jan-16), 866-874. https://doi.org/10.1016/j.neucom.2015.10.004.
  • Shah, A., Bennamoun, M., Boussaid, F. (2016). A novel feature representation for automatic 3D object recognition in cluttered scenes. Neurocomputing, 205(12-Sep-16), 1-15. https://doi.org/10.1016/j.neucom.2015.11.019.

Conference Publications

  • Shah, A., Bennamoun, M., Boussaid, F. (2016). Automatic 3D face landmark localization based on 3D vector field analysis. Proceedings of International Conference Image and Vision Computing New Zealand (6p.). IEEE Computer Society. https://doi.org/10.1109/IVCNZ.2015.7761526.

Research Projects

  • Integrating Equity, Diversity, and Inclusion (EDI) into ICT Education: A GenAI-Powered Approach to Curriculum Development, Australasian Council of Deans of Information & Communications Technology, Grant, 2025, $910.

Research Student Supervision

Current Supervisions

Principal Supervisor

  • Doctor of Philosophy, CubeSat Communications Enhanced by Movable Antennas
  • Master of Computing and Security by Research, Redundancy-Aware Reflective AI Agents
  • Doctor of Philosophy, Open-Vocabulary 3D Instance Segmentation
  • Doctor of Philosophy, Dynamic Freshness Forecasting and Produce Tracking in Smart Refrigerators: Using Artificial Intelligence Systems for Household Food Management

Co-principal Supervisor

  • Doctor of Philosophy, Using Artificial Intelligence to identify Autoantibodies as Biomarkers of Response to Immunotherapy and Immune-related Adverse Events in Cutaneous Melanoma Patients

Associate Supervisor

  • Doctor of Philosophy, Combining mass spectrometry and machine learning to discover bioactive peptides from crops

Completed Supervisions

Principal Supervisor

  • Doctor of Philosophy, Novel Computational Frameworks for Empathy Modeling and Evaluation in Artificial Agents
  • Doctor of Philosophy, Towards AI Explainability: Unraveling Black-Box Behaviour of Intelligent Agents

Associate Supervisor

  • Doctor of Philosophy, Boxing Performance Analysis: The Role of Camera Systems, Match Conditions, and Automation
  • Doctor of Philosophy, Domain shift robustness in deep learnng models
  • Doctor of Philosophy, IoMT Security: A Semantic Framework for Vulnerability Detection in Remote Patient Monitoring
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