Background and interests
Associate Professor Jeremiah Deng is interested in developing intelligent algorithms for pattern recognition, machine learning, and optimization of computer and network systems. His recent work investigates online adaptive learning algorithms for anomaly detection, scene categorization, semantic video analysis, event detection, and performance modeling and optimization of wireless networks. He has authored/co-authored more than 100 papers published in peer-reviewed journals and conference proceedings, or as book chapters. Dr. Deng is a member of ACM and IEEE, and serves on the editorial board of Cognitive Computation (Springer). He co-chairs the Machine Learning for Sensory Data Analysis (MLSDA) workshops (in conjunction with PAKDD), and has served on the program committees of a number of international conferences such as IJCAI, PRICAI, ACCV, GlobeCom, ICC and ECE.
Dr. Deng teaches a variety of undergraduate and postgraduate courses in Information Science and Telecommunications (Applied Science). He is currently the Director of the Telecommunications Programme and supports ongoing curriculum development for BAppSc/PGDip/MAppSc qualifications.
For more information, including recent publications, see his personal website (link above).
- INFO 204 Introduction to Data Science
- INFO 411 Machine Learning and Data Mining
- INFO 508 Research Project
- Sean Lee
- Ahmad Shahi
- Sophie Zareei
- Robert Hou
- Chontira Chumsaeng
Gurtner, M., Smith, M., Gage, R., Howey-Brown, A., Wang, X., Latavao, T., Deng, J. D., Zwanenburg, S. P., Stanley, J., & Signal, L. (2022). Objective assessment of the nature and extent of children’s internet-based world: Protocol for the Kids Online Aotearoa study. JMIR Research Protocols, 11(10), e39017. doi: 10.2196/39017
Tetereva, A., Li, J., Gibson, B., Deng, J., & Pat, N. (2022). Multimodal MRI predictive biomarkers for cognition across the lifespan. In K. Horne (Ed.), Proceedings of the 38th International Australasian Winter Conference on Brain Research (AWCBR). (pp. 66). Retrieved from https://www.queenstownresearchweek.org
Tetereva, A., Li, J., Deng, J. D., Stringaris, A., & Pat, N. (2022). Capturing brain-cognition relationship: Integrating task-based fMRI across tasks markedly boosts prediction and test-retest reliability. NeuroImage, 263, 119588. doi: 10.1016/j.neuroimage.2022.119588
Pang, Y., Zhang, H., Deng, J. D., Peng, L., & Teng, F. (2022). Rule-based collaborative learning with heterogeneous local learning models. In J. Gama, T. Li, Y. Yu, E. Chen, Y. Zheng & F. Teng (Eds.), Advances in knowledge discovery and data mining: Proceedings of the 26th Pacific-Asia Conference, PAKDD (Part 1): Lecture notes in artificial intelligence (Vol. 13280). (pp. 639-651). Cham, Switzerland: Springer. doi: 10.1007/978-3-031-05933-9_50
Hou, J., Ding, X., & Deng, J. D. (2022). Semi-supervised semantic segmentation of vessel images using leaking perturbations. Proceedingsof the IEEE/CFV Winter Conference on Applications of Computer Vision (WACV). (pp. 1769-1778). IEEE. doi: 10.1109/WACV51458.2022.00183