Dr. Fu Swee Tee received her BSc (Hons) in Computing from the University of Portsmouth (UoP) in 2006, followed by a Master of Advanced Information Technology from Universiti Malaysia Sarawak (UNIMAS) in 2013. She was awarded her PhD in 2025. Prior to entering academia, she worked in the IT industry as a software developer. She later transitioned into higher education, serving as a lecturer at a skills training college for over six years. She is currently a Lecturer in Computing at Swinburne University of Technology Sarawak (SUTS), under the Faculty of Engineering, Computing and Science (FECS).
Her research interests lie primarily in the application of deep learning and computer vision techniques for intelligent video analytics, with a particular focus on abnormal event detection in complex environments.
Research Interests
Artificial intelligence
Deep learning
Computer vision
Anomaly detection
Professional Membership
Australia Computer Society (ACS) Associate
PhD/Master by Research Opportunities
Potential research higher degree candidates are welcome to enquire about postgraduate opportunities in the areas listed above. Please e-mail [email protected] for more information.
Publications
S. Tee, L. B. Theng, B. L. C. Shiong, C. McCarthy and M. T. K. Tsun (2024). A Multi-Stream Approach to Mixed-Traffic Accident Recognition Using Deep Learning, IEEE Access, vol. 12, DOI: 10.1109/ACCESS.2024.3512794, pp. 185232 – 185249.
Lim, B. T. Lau, S. T. Fu, M. T. K. Tsun and F. M. Amirul Islam, “Cyberbullying Awareness and Prevention Among University Students: A Serious Digital Game Intervention,” 2024 IEEE 13th International Conference on Engineering Education (ICEED), Kanazawa, Japan, 2024, pp. 1-5, doi: 10.1109/ICEED62316.2024.10923761.
Lim, W., Lau, B.T., Fu, S.T. and Tee, M. T. K. Tsun (2024). Sociodemographic Factors Associated with Cyberbullying Experience and Practice Among Youths in Malaysia, Journal of Technology in Behavioral Science. DOI: 10.1007/s41347-024-00469-9
S. Tee, L. B. Theng, B. L. C. Shiong and M. T. K. Tsun, “The Bigger Picture: Scene Understanding for Recognizing Accidents within Mixed-Traffic Scenario using Deep Learning,” 2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia, 2023, pp. 39- 44, doi: 10.1109/ICCSCE58721.2023.10237134.
ST Fu, BT Lau, MKT Tee, BCS Loh (2022), Exploring Deep Learning in Road Traffic Accident Recognition for Roadside Sensing Technologies, Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1, 32-52
Cheah Wai Shiang, Fu Swee Tee, Alfian Abdul Halin, Ng Keng Yap, Puah Chin Hong (2018). Ontology Reuse for Multiagent System Development through Pattern Classification, Software: Practice and Experience, 48, issue 11, DOI: 10.1002/spe.2595, pp. 1923-1939.
