Professor and Canada Research Chair in Machine Learning
School of Engineering, University of Guelph
Faculty Member and Canada CIFAR AI Chair
Vector Institute for Artificial Intelligence
Academic Director, NextAI
Richards Building, Room 3515
Guelph, Ontario, N1G 2W1
To contact me by email or phone, please visit the University of Guelph Directory.
I lead the Machine Learning Research Group at the University of Guelph. My research spans a number of topics in deep learning. I am interested in open problems such as how to effectively learn with less labeled data, and how to build human-centred AI systems. I am interested in methodologies such as generative modelling, graph representation learning and sequential decision making. I also pursue applied projects that leverage computer vision to mitigate biodiversity loss.
I received my PhD in Computer Science from the University of Toronto in 2009, where I was advised by Geoffrey Hinton and Sam Roweis. I spent two years as a postdoc at the Courant Institute of Mathematical Sciences, New York University working with Chris Bregler, Rob Fergus, and Yann LeCun. In 2012, I joined the School of Engineering at the University of Guelph as an Assistant Professor. In 2017, I was promoted to Associate Professor and became a member of the Vector Institute for Artificial Intelligence. In 2018, I was honoured as one of Canada's Top 40 under 40. In 2019, I was named a Canada CIFAR AI Chair. I spent 2018-2019 as a Visiting Faculty member at Google Brain, Montreal. In 2021 I was promoted to Professor and I became the Interim Research Director at the Vector Institute. In 2022 I became Vector's Research Director. At the end of 2023, I concluded my tenure as the Research Director at the Vector Institute to focus more on my research.
A complete list of my publications is available on Google Scholar.
ZeMing Gong, Austin Wang, Xiaoliang Huo, Joakim Bruslund Haurum, Scott Lowe, Graham Taylor, and Angel Chang. CLIBD: Bridging vision and genomics for biodiversity monitoring at scale. In International Conference on Learning Representations (ICLR), 2025. To appear. Early version appeared at Computer Vision and Pattern Recognition (CVPR) Workshop on Fine-Grained Visual Categorization. [ bib | arXiv | GitHub | http ]
Laura Pollock, Justin Kitzies, Sara Beery, Kaitlyn Gaynor, Marta Jarzyna, Oisin Mac Aodha, Bernd Meyer, David Rolnick, Graham Taylor, Devis Tuia, and Tanya Berger-Wolf. Harnessing artificial intelligence to fill global shortfalls in biodiversity. Nature Reviews, 2025. [ bib | DOI ]
Tiancheng Gao and Graham Taylor. BarcodeMamba: State space models for biodiversity analysis. In Neural Information Processing Systems (NeurIPS) Workshop on Foundation Models for Science, 2024. [ bib | arXiv | GitHub ]
Zahra Gharaee, Scott Lowe, ZeMing Gong, Pablo Millan Arias, Nicholas Pellegrino, Austin Wang, Joakim Bruslund Haurum, Iuliia Zarubiieva, Lila Kari, Dirk Steinke, Graham Taylor, Paul Fieguth, and Angel Chang. BIOSCAN-5M: A multimodal dataset for insect biodiversity. In Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2024. [ bib | arXiv | GitHub ]
Joakim Bruslund Haurum, Sergio Escalera, Graham Taylor, and Thomas Moeslund. Agglomerative token clustering. In European Conference on Computer Vision (ECCV), 2024. [ bib | arXiv | GitHub | http ]
Pablo Millan Arias, Niousha Sadjadi, Monireh Safari, ZeMing Gong, Austin Wang, Scott Lowe, Joakim Bruslund Haurum, Iuliia Zarubiieva, Dirk Steinke, Lila Kari, Angel Chang, and Graham Taylor. BarcodeBERT: Transformers for biodiversity analysis. In Neural Information Processing Systems (NeurIPS) Workshop on Self-Supervised Learning: Theory and Practice, 2023. [ bib | arXiv | GitHub ]
Zahra Gharaee, ZeMing Gong, Nicholas Pellegrino, Iuliia Zarubiieva, Joakim Bruslund Haurum, Scott Lowe, Jaclyn McKeown, Chris Ho, Joschka McLeod, Yi-Yun Wei, Jireh Agda, Sujeevan Ratnasingham, Dirk Steinke, Angel Chang, Graham Taylor, and Paul Fieguth. A step towards worldwide biodiversity assessment: The BIOSCAN-1M insect dataset. In Advances in Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2023. [ bib | arXiv | GitHub ]
Michal Lisicki, Mihai Nica, and Graham Taylor. Bandit-driven batch selection for robust learning under label noise. In Neural Information Processing Systems (NeurIPS) Workshop on Optimization for Machine Learning, 2023. Also presented at the NeurIPS Workshop on Advancing Neural Network Training (WANT). [ bib | arXiv ]
Joakim Bruslund Haurum, Sergio Escalera, Graham Taylor, and Thomas Moeslund. Which tokens to use? investigating token reduction in vision transformers. In International Conference on Computer Vision (ICCV) Workshop on New Ideas in Vision Transformers, 2023. [ bib | arXiv | GitHub | http ]
Kevin Kasa and Graham Taylor. Empirically validating conformal prediction on modern vision architectures under distribution shift and long-tailed data. In International Conference on Machine Learning (ICML) Workshop on Structured Probabilistic Inference & Generative Modeling, 2023. [ bib | arXiv ]
Mateusz Jurewicz, Graham Taylor, and Leon Derczynski. The Catalog Problem: Clustering and Ordering Variable-Sized Sets. In International Conference on Machine Learning (ICML), 2023. [ bib | DOI | .pdf ]
Cong Wei, Brendan Duke, Ruowei Jiang, Parham Aarabi, Graham Taylor, and Florian Shkurti. Sparsifiner: Learning Sparse Instance-Dependent Attention for Efficient Vision Transformers. In Conference on Computer Vision and Pattern Recognition (CVPR), 2023. [ bib | arXiv | GitHub | http ]
Angus Galloway, Anna Golubeva, Mahmoud Salem, Mihai Nica, Yani Ioannou, and Graham Taylor. Bounding generalization error with input compression: An empirical study with infinite-width networks. Transactions on Machine Learning Research (TMLR), 2022. [ bib | arXiv ]
Rylee Thompson, Boris Knyazev, Elahe Ghalebi, Jungtaek Kim, and Graham Taylor. On evaluation metrics for graph generative models. In International Conference on Learning Representations (ICLR), 2022. [ bib | arXiv | GitHub ]