Dr Zawar Hussain
Course Coordinator and Senior Lecturer, Master of Data Science
An accomplished academic with extensive teaching experience, having taught a wide range of undergraduate and graduate courses in computer science and engineering. Dr Zawar is also a dedicated researcher with a focus on pervasive computing, human activity recognition, and the Internet of Things (IoT) in healthcare applications. His PhD research at Macquarie University explored the use of IoT sensor technologies and big data analytics to enhance healthcare delivery. Dr Zawar has received numerous awards and grants for his work, including seed funding from the Centre for Critical Digital Infrastructure (CCDI) and the International Research Training Program (IRTP) scholarship.
· Registered member of the Association for Computing Machinery (ACM)
· Member of the European Alliance for Innovation (EAI)
· Member of the Intelligent Computing Laboratory (ICL) at Macquarie University, Australia.
· Pakistan Engineering Council Registered Member.
· Member of the Telecommunication and Networking (TELECON) research group at GIK Institute, Pakistan.
MDS601 Programming Foundations for Data Science
MDS604 Database Systems and Infrastructure
MDS650 Data Science Capstone Project
MDS651 Industrial Experience Project
Publications
- Das, M., Hussain, Z. and Nawaz, M., 2026. Latency-Aware Benchmarking of Large Language Models for Natural-Language Robot Navigation in ROS 2. Sensors, 26(2), p.608.(link)
- Nawaz, M., Nasir, B., Zia, T., Hussain, Z. and Moreira, C., 2026. TagGAN: A generative model for data tagging. Computers in Biology and Medicine, 200, p.111385. (link)
- Batool, H., Nasir, B., Ijaz, H,M,T., Nawaz, M. and Hussain, Z., 2025, December. CMuA-Net: Context‑Aware Multi‑Attention U‑Net with ConvLSTM Blocks. In 2025 International Conference on Frontiers of Information Technology (FIT) (pp. 1-6). IEEE. (link)
- Wahab, A., Ijaz, H,M,T., Nasir, B., Hussain, Z. and Nawaz, M., 2025, December. ConvSE for Multiple Sclerosis Lesion Segmentation. In 2025 International Conference on Frontiers of Information Technology (FIT) (pp. 1-6). IEEE. (link)
- Akbar, M.S., Hussain, Z., Ikram, M., Sheng, Q.Z. and Mukhopadhyay, S., 2024. On challenges of sixth-generation (6G) wireless networks: A comprehensive survey of requirements, applications, and security issues. Journal of Network and Computer Applications, p.104040.
- Akbar, M.S., Hussain, Z., Sheng, Q.Z., and Shankaran, R., 2022, Wireless Body Area Sensor Networks: Survey of MAC and Routing Protocols for Patient Monitoring under IEEE 802.15.4 and IEEE 802.15.6. Sensors, 22(21), p.8279.
- Hussain, Z., Sheng, Q.Z., Zhang, W.E., Ortiz, J., and Pouriyeh. S., 2022, Unobtrusive techniques for monitoring different aspects of sleep: A Comprehensive Review: ACM Transaction on Computing for Healthcare (HEALTH). 3(2), pp.1-26.
- Hussain, Z., Waterworth, D., Mahmood, A., Sheng, Q.Z. and Zhang, W.E., 2021. Dataset for Toothbrushing Activity Using Brush-Attached and Wearable Sensors. Data in Brief, p.107248.
- Hussain, Z., Sheng, Q.Z., Zhang, WE. 2020. A Review and Categorization of Techniques on Device-Free Human Activity Recognition. Journal of Network and Computer Application (JNCA), Elsevier, Vol 167, Article 102738, 2020.
- Hussain, Z., Abbas, G. and Halim, Z., 2017. A Lightweight Fairness-Driven AQM for Regulating Bandwidth Utilization in Best-Effort Routers. Journal of Network and Systems Management, pp.1-32.
- Aldeer, M., Waterworth, D., Hussain, Z., Chowdhury, T., Brito, C., Sheng, Q.Z., Martin, R.P., and Ortiz, J., 2022, May. MedBuds: In-Ear Inertial Medication Taking Detection Using Smart Wireless Earbuds. In 2nd International Workshop on Cyber-Physical-Human System Design and Implementation (CPHS 2022) (pp. 19-23).
- Akbar, M.S., Hussain, Z., Sheng, Q.Z., and Mukhopadhyay, S., 2021, An Enhancement to Channel Access Mechanism for the IEEE 802.15.3C MillimeterWave (5G) Standard to Support Stringent QoS Requirements of IoT. In International Conference on Internet of Things as a Service (IoTaaS 2021) (pp. 19-38). Springer, Cham.
- Hussain, Z., Waterworth, D., Aldeer, M., Zhang, W.E. Sheng, Q.Z., and Ortiz, J., 2021, Do You Brush Your Teeth Properly? An Off-body Sensor-based Approach for Toothbrushing Monitoring. In Proceedings of the IEEE International Conference on Digital Health (ICDH 2021) Virtual Conference, September 5-10, 2021. (pp. 59-69). (Core Rank B)
- Hussain, Z., Waterworth, D., Aldeer, M., Zhang, W.E. and Sheng, Q.Z., 2020, November. Toothbrushing data and analysis of its potential use in human activity recognition applications: Dataset. In Proceedings of the Third Workshop on Data: Acquisition To Analysis (DATA ’20). (pp. 31-34).
- Hussain, Z., Sagar, S., Zhang, W.E. and Sheng, Q.Z., 2019, November. A cost-effective and non-invasive system for sleep and vital signs monitoring using passive RFID tags. In Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous
- Systems: Computing, Networking and Services (MobiQuitous 2019). (pp. 153-161). (Core Rank B)
- Lu, J., Zheng, X., Sheng, Q.Z., Hussain, Z., Wang, J. and Zhou, W., 2019, November. MFE-HAR: multiscale feature engineering for human activity recognition using wearable sensors. In Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2019). (pp. 180-189). (Core Rank B)
- Tran, D. H., Hussain, Z., Zhang, W.E., Khoa, N.L.D., Tran, N.H. and Sheng, Q.Z., 2018, January. Deep autoencoder for recommender systems: parameter influence analysis. In 29th Australasian conference on information systems, (ACIS 2018) (pp. 1-12).
- Hussain, Z., Abbas, G. and Raza, U., 2015, December. Choke with recent drop history. In Proceedings of 13th International Conference on Frontiers of Information Technology (FIT) (pp. 160-165). IEEE.
- Raza, U., Abbas, G. and Hussain, Z., 2015, December. CHOKe-FS: CHOKe with fair bandwidth share. In Proceedings of 2015 International Conference on Information and Communication Technologies (ICICT), (pp. 1-5). IEEE.
Fields of Research
Pervasive Computing, Human Activity Recognition, Digital Health, Remote Healthcare, Internet of Things and Machine Learning