Osmar R. Zaïane
Appointment
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2021
Osmar Zaïane is a Canada CIFAR AI Chair, a fellow at Amii, and a professor at the University of Alberta’s Department of Computing Science. He served as Amii’s Scientific Director from 2009 to 2020.
Zaïane focuses on pattern discovery and information extraction from large databases, also known as data mining. His work involves data mining from disparate heterogeneous data sources, such as on the Internet, as well as the analysis of complex information networks, also known as social network analysis. He focuses on building applications that can improve decision making in fields from business to medicine, allowing decisions to be based on data and data analysis. Through the application of machine learning and methods of knowledge discovery, he devises ways to personalize applications, automate processes and improve upon current data science practices.
Awards
- Fellow of the Asia-Pacific Artificial Intelligence Association, Asia-Pacific Artificial Intelligence Association, 2023
- Fellow of the Canadian Academy of Engineering, Canadian Academy of Engineering, 2023
- CS-Can|Info-Can Lifetime Achievement Award, 2023
- Great Supervisor Award, University of Alberta, 2018
- ACM – SIGKDD Service Award, 2010
- IEEE – ICDM Outstanding Service Award, 2009
- Killam Professorship, 2009
- McCalla Research Professorship, 2008
Relevant Publications
- Wang, H., Cao, P., Yang, J., & Zaiane, O. (2024). Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation. Neural Networks, 106546. Pergamon.
- Liu, L., Wen, G., Cao, P., Hong, T., Yang, J., Zhang, X., & Zaiane, O. R. (2023). BrainTGL: A dynamic graph representation learning model for brain network analysis. Computers in Biology and Medicine, 153, 106521. Pergamon.
- Wang, H., Cao, P., Wang, J., & Zaiane, O. R. (2022). UCTRANSNET: Rethinking the skip connections in U-Net from a channel-wise perspective with transformer. Proceedings of the AAAI Conference on Artificial Intelligence, 36(3), 2441–2449.
- Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O. R., & Jagersand, M. (2020). U2-Net: Going deeper with nested U-structure for salient object detection. Pattern Recognition, 106, 107404. Pergamon.
Jiang, H., Cao, P., Xu, M., Yang, J., & Zaïane, O. (2020). Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction. Computers in Biology and Medicine, 127, 104096.