阿扎爾 博士
Dr. Muhammad, AZHAR
Associate Head & Assistant Professor
Programme Director of BSc-ADS
Director of Big Data Laboratory
 
 
Dr. Muhammad Azhar is the Assistant Professor, Associate Head, Programme Director of the BSc in Applied Data Science, and Director of the Big Data Laboratory at Hong Kong Shue Yan University. His expertise covers Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision.

He has experience across academia and industry, including roles at Sofnix Private Limited and Turnotech Private Limited in Pakistan and Welltime Ltd. in the U.K. He holds a PhD in Machine Learning and Data Mining from Shenzhen University, China, and an MS in Virtual Reality from Sejong University, South Korea. Before joining HKSYU in 2023, he served as a Research Professor in South Korea, contributing to projects on 3D dental X-ray reconstruction and osteoporosis prediction using multimodal data.

A dedicated AI educator, Dr. Azhar has taught diverse courses from introductory programming to advanced machine learning. He received the University’s 2024–2025 Teaching Excellence Award and achieved top student evaluation scores for consecutive semesters. He also initiated and launched the department’s first Minor programme.

His research appears in Applied Soft Computing, Applied Intelligence, IEEE Access, and other high-impact journals. He leads funded projects including “MindMate,” a generative AI system for personalized mental health, and a QESS grant on AI-based language learning.

Courses Taught

  • Introduction to Machine Learning (ML)
  • Deep Learning
  • Computer Vision with Deep Learning
  • Computer Organization and Architecture (COA)
  • Digital Humanities: Theories and Methods
  • Introduction to Database Systems
  • Computer Programming as Problem Solving
  • Predicting Human Behavior with Big Data
  • Writing Your First Apps Using Python Programs
  • Introduction to Data Science
  • Python for Data Science
  • Introduction to Data Mining

Area of Interest

  • Machine Learning
  • Deep Learning
  • Data Mining
  • Computer Vision
  • Natural Language Processing
 

Academic Qualifications

  • PhD (Machine Learning and Data Mining) Big Data Institute, Shenzhen University, Shenzhen, China
  • MS (Virtual Reality) Intelligent Media Lab, Sejong University, South Korea
  • BS (Computer Science) National University of Computer and Emerging Sciences, Islamabad, Pakistan
 

Funded Projects

  1. HKSTP (HK$100,000) – Principal Investigator (PI)
    Duration: January 2025 – December 2025
    Project Title: A Health Buddy Bot for Enhancing the Well-Being of Hong Kong Citizens
    Funding Body: Hong Kong Science & Technology Parks Corporation (HKSTP/24-1596)
    This project develops an AI-powered wellness chatbot that promotes holistic well-being through data driven dialogue and personalized health recommendations.
     
  2. URG (HK$40,000) – Principal Investigator (PI)
    Duration: January 2024 – December 2026
    Project Title: MindMate: A Generative AI System for Personalized Mental Health Recommendations through Chat Interaction
    This project designs a transformer-based conversational AI to provide empathetic mental health support and personalized guidance.
     
  3. RIS (HK$15,000) – Principal Investigator (PI)
    Duration: January 2024 – December 2027
    Project Title: Automatic Suggestions Generation by Generative Artificial Intelligence Based on Customer Reviews This project focuses on developing a generative AI model that converts customer feedback into actionable business insights.
     
  4. QESS Project 1 (HK$13,142,800) – Leader of Technical Development Team
    Duration: January 2026 – December 2028
    Project Title: Developing an AI-Enhanced Teaching Resource Hub for the Self-Financing Higher Education Sector
    A large-scale project creating an AI platform to curate and personalize teaching materials across Hong Kong’s self-financing institutions.
     
  5. QESS Project 2 (HK$2,382,950) – Leader of the Technology Support Team
    Duration: January 2025 – December 2026
    Project Title: Empowering Language Competence and Professional Communication through an AI Based Language Laboratory (ALL)
    This project develops an AI-enabled language learning system that provides interactive feedback to enhance communication competence.
     
  6. QESS Project 3 (HK$3,936,800) – Co-Leader of Technical Development Team
    Duration: January 2026 – December 2027
    Project Title: Enhancing Students’ Emotional Wellness and Resilience in Post-Secondary Institutions
    This project implements AI-based predictive analytics for monitoring and enhancing student emotional resilience.
     
  7. QESS project 4 (HK$18,656,550) – Technical Team Member
    Duration: January 2026 – December 2029 Project Title: Developing an AI-Enhanced Teaching Resource Hub for the Self-Financing Higher Education Sector
    A territory-wide collaboration applying AI-driven content management and personalization to teaching resources in higher education.
     
  8. External Fund – Korea (₩30,000,000) – Team Member
    Duration: January 2022 – December 2023
    Project Title: Classification of Dental Diseases through Panoramic Radiographs
    Developed deep learning models for dental disease classification using panoramic radiographs, focusing on model optimization and validation.
 

Publications

Refereed Journal Papers
  1. Azhar, M.*, Amjad, A., Farid, G., Dewi, D. A., & Batumalay, M. (2025). Efficient Transformer-Based Abstractive Urdu Text Summarization Through Selective Attention Pruning. Information, 16(11), 991 (IF: 2.9, Q2)
  2. Azhar, M.*, Amjad, A., Dewi, D. A., & Kasim, S. (2025). A Systematic Review and Experimental Evaluation of Classical and Transformer-Based Models for Urdu Abstractive Text Summarization. Information, 16(9). (IF: 2.9, Q2)
  3. Cheemaa, A. S., Azhar, M.*, Arif, F., ul haq, Q. M., Sohail, M., & Iqbal, A. (2025). EGPT-SPE: story point effort estimation using improved GPT-2 by removing inefficient attention heads. Applied Intelligence, 55(15), 994. (IF: 3.5, Q2)
  4. Chen, J., Xu, Y., Li, X., & Azhar, M. (2025). A mode-partitioned gamma mixture model estimation method for large-scale multimodal data. Big Data Mining and Analytics, 9(1), 4-22. (IF: 6.2, Q1)
  5. Farid, G., Bilal, M., Zhang, L., Alharbi, A., Ahmed, I., & Azhar, M. (2025). An improved deep Q-Learning approach for navigation of an autonomous UAV agent in 3D Obstacle-Cluttered environment. Drones, 9(8), 518. (IF: 4.8, Q1)
  6. Shakeel, H., Akram, M., Javeed, M. U., Azhar, M.*, Aslam, S. M., & Mumtaz, M. T. (2025). LncRNAs Disease: A text mining Approach to Find the role of lncRNA in Aging. Journal of Computing & Biomedical Informatics, 9(01).
  7. Khan, M., Hussain, S., Amjad, A., Jamil, A., Azhar, M., Usman, M., ... & Akbar, M. H. (2025). Enhancing human-centric interaction: A deep learning approach for robust facial expression recognition and intensity estimation. Spectrum of Engineering Sciences, 1976-1985.
  8. Azhar, M., Perveen, S., Iqbal, A., & Lee, B. (2024). IDRandom-Forest: advanced random forest for real-time intrusion detection. IEEE Access. (IF: 3.6, Q1)
  9. Khan, S. U., Khan, M. A., Azhar, M., Khan, F., Lee, Y., & Javed, M. (2023). Multimodal medical image fusion towards future research: A review. Journal of King Saud University-Computer and Information Sciences, 35(8), 101733. (IF: 6.1, Q1)
  10. Zia, S., Azhar, M.*, Lee, B., Tahir, A., Ferzund, J., Murtaza, F., & Ali, M. (2023). Recognition of printed Urdu script in Nastaleeq font by using CNN-BiGRU-GRU based encoder-decoder framework. Intelligent Systems with Applications, 18, 200194. (IF: 4.3, Q1)
  11. Yasin, A., Fatima, R., Wen, L., Afzal, W., Azhar, M., & Torkar, R. (2020). On using grey literature and google scholar in systematic literature reviews in software engineering. IEEE access, 8, 36226-36243. (IF: 3.4, Q1)
  12. Zhang, X., He, Y., Jin, Y., Qin, H., Azhar, M., & Huang, J. Z. (2020). A Robust k‐Means Clustering Algorithm Based on Observation Point Mechanism. Complexity, 2020(1), 3650926. (IF: 1.7, Q1)
  13. Trinh, T., Wu, D., Huang, J. Z., & Azhar, M. (2020). Activeness and loyalty analysis in event-based social networks. Entropy, 22(1), 119. (IF: 2, Q2)
  14. Azhar, M.*, Huang, J. Z., Masud, M. A., Li, M. J., & Cui, L. (2020). A hierarchical Gamma Mixture Model-based method for estimating the number of clusters in complex data. Applied Soft Computing, 87, 105891. (IF: 8.26, Q1)
  15. Tahir, M., Li, M., Zheng, X., Carie, A., Jin, X., Azhar, M., ... & Hulio, Z. H. (2019). A novel network user behaviors and profile testing based on anomaly detection techniques. International Journal of Advanced Computer Science and Applications, 10(6), 305-324. (IF: 0.9, Q3)
  16. Azhar, M., Li, M. J., & Zhexue Huang, J. (2019). A hierarchical gamma mixture model-based method for classification of high-dimensional data. Entropy, 21(9), 906. (IF: 2, Q2)
  17. Baik, S. W., Azha, M., Fahad, M. S., Mehmood, I., Gu, B. W., Par, W. J., ... & Jang, Y. (2015). Flowchart Based Storyboard System for Authoring Visual Contents in Mixed Reality Space. International Journal of Electronics and Electrical Engineering, 3(3), 240-244.
  18. Azhar, M., Sajjad, M., Mehmood, I., Gu, B. W., Kim, W., Han, J. S., ... & Baik, S. W. (2014). Real time distributed content rendering technique based on agent-mediator communication framework for multi-display systems. IERI Procedia, 6, 2-7.
Authored books or book chapters
  1. Muhammad Azhar*, Ifra Shabbir, Muhammad Shafqat Ali, Ghulam Farid, Hamza Arif, and Rubia Fatima. “Integrating Agentic AI Ethically into Geriatric Healthcare: A Framework for Frailty Monitoring with Transformer Models and Wearable Technology”, In Press (2025)
  2. Saber, A., Emara, T., Mahmud, M. S., Azhar, M., & Hassan, E. (Eds.). (2025). Advancements in speech processing for human-computer interaction. IGI Global. https://www.igi-global.com/book/advancements-speech-processing-human-computer/370394
  3. Fahad, Azhar, M., Sajjad, M., Mehmood, I., Kwon, S. I., Lee, J. W., & Baik, S. W. (2014). Comparative Analysis of Graphic Contents Rendering Techniques in a Multi-view System through Agent-Mediator Based Communication. In Ubiquitous Information Technologies and Applications: CUTE 2013 (pp. 573-579). Berlin, Heidelberg: Springer Berlin Heidelberg.
Refereed Conference Papers 
  1. Azhar, M.*, Amjad, A., Mahmud, M. S., Sadiq, M., Ali, Z., & Hussain, S. (2025, December). Predictive Modeling of Alzheimer’s Disease Progression Using Multiomics and Neuroimaging Data with Transformer Architectures on the ADNI Dataset. In 2025 International Conference on Frontiers of Information Technology (FIT) (pp. 1-6). IEEE.
  2. Shabbir, I., Azhar, M.*, Ali, M. S., Ling, C. K., Wattoo, W. A., & Iqbal, A. (2025, August). Caries Detection in Dental Imaging Using Vision Transformer and Explainable AI. In 2025 International Conference on Artificial Intelligence for Sustainable Innovation (AI-SI) (pp. 1-6). IEEE.
  3. Iqbal, A., Azhar, M.*, Ali, M. S., Usman, M., Wattoo, W. A., & Farhan, M. (2025, August). Smart Fire Vision: Advancing Fire Detection in Smart Cities by Efficient Hybrid Deep Learning Technique. In 2025 International Conference on Artificial Intelligence for Sustainable Innovation (AI-SI) (pp. 1-6). IEEE.
  4. Javeed, M. U., Ali, M. S., Iqbal, A., Azhar, M.*, Aslam, S. M., & Shabbir, I. (2024, December). Transforming Heart Disease Detection with BERT: Novel Architectures and Fine-Tuning Techniques. In 2024 International Conference on Frontiers of Information Technology (FIT) (pp. 1-6). IEEE.
  5. Hussain, K., Azhar, M.*, Lee, B., Iqbal, A., Affan, M., & Khan, S. U. (2023, December). ASAnalyzer: Attention based sentiment analyzer for real-world sentiment analysis. In 2023 International Conference on Frontiers of Information Technology (FIT) (pp. 184-189). IEEE.
  6. Ali, M. S., Azhar, M.*, Masood, S., Lee, B., Iqbal, T., & Amjad, A. (2023, December). Efficient Video Summarization with Hydra Attentive Vision Transformer. In 2023 International Conference on Frontiers of Information Technology (FIT) (pp. 196-201). IEEE.
  7. Ahmad, R. A., Azhar, M.*, & Sattar, H. (2022, December). An image captioning algorithm based on the hybrid deep learning technique (CNN+ GRU). In 2022 International Conference on Frontiers of Information Technology (FIT) (pp. 124-129). IEEE.
  8. Azhar, M., & Li, J. L. (2017). IniceMPRO: Identify the Number of Clusters and Initial Cluster Centers using Multi Layer GMMs. In The 5th CCF BIG DATA Conference.
  9. Azhar, M., Malik, F. R., Sajjad, M., Irfan, M., Gu, B. W., Park, W. J., & Baik, S. W. (2014). VIP-emulator: to design interactive architecture for adaptive mixed reality space. In Proceedings of the International Conference Data Mining, Civil and Mechanical Engineering (ICDMCME 2014), International Institute of Engineers, Indonesia.
 

 

 

Professional Experiences

  • Assistant Professor, Department of Applied Data Science, Hong Kong Shue Yan University (2023 – Now)
  • Research Professor, IT Convergence, Department of Computer Science, Chosun University, South Korea (2022-2023)
  • Assistant Professor, Department of Computer Science, Comsats University Islamabad, Pakistan (2020-2022)
  • Research Scholar, Big Data Institute, Shenzhen University, Shenzhen, China (2015-2020)
  • Lecturer, Department of Computer Science, Comsats University Islamabad, Pakistan (2014-2015)
  • Research Assistant, Intelligent Media Lab, Sejong University, South Korea (2012-2014)
  • Visiting Lecturer, Huazhong University of Science and Technology, China
  • Software Engineer (Remote), Welltime Pvt. Limited, UK (2012-2014)
  • IOS and android Application developer, Turnotech Pvt. Limited, Islamabad (2011-2012)
  • Software Engineer, Sofnix Private Limited, Islamabad (2011-2012)

Scholarly and Community Services

  • Life-time member of Minhaj-ul-Quran International (Social and educational NGO in 57 countries)
  • Member of Hong Kong Computer Society
  • Reviewer of the following journals:
    • Information Sciences
    • Neural Computing and Applications
    • Applied Soft Computing
    • Computers in human behavior
    •  Intelligent systems with applications
    • IEEE ACCESS
    • Entropy