Guillaume Rabusseau: h-index, Total Citations, and Citation Map
Guillaume Rabusseau's h-index is 20 (31 i10-index, 1,626+ total citations across 97+ publications) according to Google Scholar as of June 2026. Guillaume Rabusseau is affiliated with Assistant Professor - Canada CIFAR AI Chair, Université de Montréal / Mila.
Guillaume Rabusseau is a researcher affiliated with Assistant Professor - Canada CIFAR AI Chair, Université de Montréal / Mila, specializing in Machine Learning, Tensors, Weighted Automata. Their work has been cited 1,626 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Guillaume Rabusseau's Citation Metrics
Bibliometric impact based on 97 indexed publications.
- H-Index
- 20
- i10-Index
- 31
- Total Citations
- 1,626
- Citing Countries
- 49
As of June 2026.
Guillaume Rabusseau has an h-index of 20 and 1,626 total citations across 97 publications, with research cited by institutions in 49 countries.
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Global Impact Map
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Temporal graph benchmark for machine learning on temporal graphs
2023254
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Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
75 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational framework for low-rank regression with tensor responses, subsequently extending this methodology to graph neural networks and theoretical bounds for tensor network models.
The researcher established a foundational benchmark for temporal graph machine learning, subsequently expanding the framework to knowledge graphs and unifying model perspectives.
The researcher advanced spectral learning for weighted automata, establishing theoretical links to tensor networks and RNNs while optimizing approximation methods.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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