Boris Knyazev
Co-Founder at Université de Montréal
Montreal, Quebec, Canada
Summary
👤
Senior🎓
Top SchoolBoris Knyazev is a research scientist and adjunct professor based in Montreal with a decade of experience at the intersection of machine learning and computer vision. He currently leads research at Samsung AI Lab Montreal and teaches at Université de Montréal, bringing deep academic rigor from a PhD and a track record of internships at top labs including Facebook AI and Mila. His contributions to the PyTorch Geometric project and work on graph neural networks reflect hands-on open-source impact beyond traditional vision tasks. Earlier industry roles delivered top-tier results in face and emotion recognition competitions, demonstrating an ability to translate research into high-performing systems. Known for blending classical image-processing expertise with modern deep learning, he often bridges theory and practical deployment in large-scale research settings.
10 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD Engineering, Doctor of Philosophy - PhD Engineering at University of GuelphEngineer’s Degree Information Technology, Engineer’s Degree Information Technology at Bauman Moscow State Technical University
English, French
Github Skills (7)
pytorch10
machine-learning10
deep-learning10
pytorch-geometric10
graph-neural-network10
graph-convolutional-networks10
python10
Programming languages (4)
Github contributions (5)
pyg-team/pytorch_geometric
Dec 2018 - Jan 2020
Graph Neural Network Library for PyTorch
Role in this project:
ML Engineer
ML EngineerContributions:9 commits, 3 PRs, 6 comments in 1 year
Contributions summary:Boris contributed to the PyTorch Geometric library, primarily focusing on features related to graph neural networks. Their work includes implementing a flag for using continuous node attributes in the `TUDataset`, adding examples for `COLORS` and `TRIANGLES` datasets, and adjusting pooling layers such as `TopKPooling` and `SAGPooling`. The commits also include pycodestyle formatting and addition of a transformer for node attention.
graph-neural-networkpytorchgeometric-deep-learninggraph-neural-networksdeep-learning
bknyaz/graph_attention_pool
May 2019 - Oct 2020
Attention over nodes in Graph Neural Networks using PyTorch [NeurIPS 2019]
Contributions:75 commits, 49 pushes, 1 branch in 1 year 5 months
graph-neural-networkpytorchgraphgraph-neural-networksneurips
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