Graham Taylor
Appointment
Canada CIFAR AI Chair
CIFAR Azrieli Global Scholar 2016-2018
Learning in Machines & Brains
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2017
Renewed Canada CIFAR AI Chair – 2023
Graham Taylor is a Canada CIFAR AI Chair at the Vector Institute, a CIFAR Azrieli Global Scholar 2016-2018, a Canada Research Chair in Machine Learning, a professor at the school of engineering at the University of Guelph, and an academic director at NextAI. He was research director at the Vector Institute between 2021-2023.
Taylor’s research spans a number of topics in deep learning. He is interested in open problems such as how to effectively learn with less labeled data, and how to build human-centred AI systems. He is interested in methodologies such as generative modelling, graph representation learning and sequential decision making. He also pursues applied projects that leverage computer vision to mitigate biodiversity loss. He co-organizes the annual CIFAR Deep Learning + Reinforcement Learning Summer School (DLRLSS), and has trained more than 80 students and staff members on AI-related projects.
Awards
- Arthur C. Stern Distinguished Paper Award, Air & Waste Management Association, 2024
- Outstanding Service Award, Vector Institute, 2021
- Canada’s Top 40 Under 40, 2018
Relevant Publications
- Gharaee, Z., Gong, Z., Pellegrino, N., Zarubiieva, I., Haurum, J. B., Lowe, S. C., McKeown, J. T. A., Ho, C. C. Y., McLeod, J., Wei, Y.-Y. C., Agda, J., Ratnasingham, S., Steinke, D., Chang, A. X., Taylor, G., & Fieguth, P. W. (2023). A step towards worldwide biodiversity assessment: The BIOSCAN-1M insect dataset. In Advances in Neural Information Processing Systems.
- Jurewicz, M., Taylor, G., & Derczynski, L. (2023). The catalog problem: Clustering and ordering variable-sized sets. In International Conference on Machine Learning (ICML).
- Wei, C., Duke, B., Jiang, R., Aarabi, P., Taylor, G., & Shkurti, F. (2023). Sparsifiner: Learning sparse instance-dependent attention for efficient vision transformers. In Conference on Computer Vision and Pattern Recognition (CVPR).
- Thompson, R., Knyazev, B., Ghalebi, E., Kim, J., & Taylor, G. (2022). On evaluation metrics for graph generative models. In International Conference on Learning Representations (ICLR).
- Knyazev, B., Drozdzal, M., Taylor, G., & Romero-Soriano, A. (2021). Parameter prediction for unseen deep architectures. In Neural Information Processing Systems (NeurIPS).