PeopleofSuperintelligence

Researcher

Guillaume Rabusseau

Guillaume Rabusseau is a machine learning researcher who works on tensors and tensor networks as tools for learning from structured data. After a PhD at Aix-Marseille University in 2016 and a postdoctoral fellowship in McGill's Reasoning and Learning Lab, he joined Université de Montréal and Mila in September 2018. In 2018, he coauthored a paper with Tianyu Li and Doina Precup showing that weighted finite automata and second-order recurrent networks with linear activations are expressively equivalent, and in 2019 he received a Canada CIFAR AI Chair. The Temporal Graph Benchmark of 2023 added datasets for temporal graphs, and the 2026 Tensor Cookbook offers a guide to tensor networks. He is an associate professor at Université de Montréal and a core member of Mila.

Timeline

  1. 2016

    A PhD at Aix-Marseille

    He earns a PhD from Aix-Marseille University in 2016, working in the Qarma team (Machine Learning and Multimedia) under the supervision of François Denis and Hachem Kadri. His MSc in fundamental computer science and his BSc in computer science come from the same university.

  2. By 2018

    A postdoc at McGill

    He works as an IVADO postdoctoral research fellow in the Reasoning and Learning Lab at McGill University, with Prakash Panangaden, Joelle Pineau and Doina Precup.

  3. 2018

    Weighted automata and recurrent networks

    In 2018, he coauthors a paper with Tianyu Li and Doina Precup showing that weighted finite automata and second-order recurrent neural networks with linear activation functions are expressively equivalent on sequences of discrete symbols. The paper also proposes a provable learning algorithm for linear second-order RNNs over sequences of continuous input vectors.

  4. September 2018

    Joining Université de Montréal

    He becomes an assistant professor at Mila – Quebec Artificial Intelligence Institute and in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal in September 2018. His research covers tensor methods for machine learning and learning algorithms for structured data built on linear and multilinear algebra.

  5. March 2019

    A Canada CIFAR AI Chair

    He is awarded a Canada CIFAR AI Chair in March 2019.

  6. 2023

    The Temporal Graph Benchmark

    In 2023, he coauthors the Temporal Graph Benchmark (TGB) with Shenyang Huang, Reihaneh Rabbany, Jure Leskovec, Michael Bronstein and others. TGB is a collection of benchmark datasets for evaluating machine learning models on temporal graphs.

  7. May 2026

    The Tensor Cookbook

    In May 2026, he and Beheshteh T. Rakhshan post Tensor Cookbook: Mastering Tensors through Diagrams on arXiv, a self-contained guide to tensor networks and their use in tensor algebra.

  8. As of October 2026

    Associate professor at Université de Montréal

    He is an associate professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal, a core member of Mila and a Canada CIFAR AI Chair. CIFAR renews his chair in 2024.

Career

  1. Since 2018Université de MontréalAssociate Professor
  2. Since 2018MilaAssistant Professor
  3. Until 2018McGill UniversityPostdoctoral Research Fellow

Education

  1. 2016Aix-Marseille UniversitéPhD

From the biography.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes Université de Montréal. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  2. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes Université de Montréal. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.
  3. Universities (31%) and startups and other companies (25%) employ the largest shares of 2020–2026 entrantsIncludes Mila. Among 819 people whose first recorded role started in 2020–2026 and who have a known current organization, 60% work in commercial sectors (25% at startups and other companies, 23% at frontier labs and 13% at big tech, with 9 in two), and 31% work at universities, compared with 66% for earlier entrants.
  4. Companies supply the most prior-work hires at 7 of the 9 frontier AI labsIncludes Mila. Our estimates of the organizations where 550 people worked directly before joining 9 frontier AI labs across 620 career transitions, comparing the main feeder organizations per lab.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Guillaume Rabusseau | Mila mila.quebec. Checked 21 October 2026.
  2. Séminaire départemental – Tensor Networks for Machine Learning vendredi 28 avril 2023 – CRTI – Université Laval crti.ift.ulaval.ca. Checked 21 October 2026.
  3. [1807.01406] Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning arxiv.org. Checked 21 October 2026.
  4. Guillaume Rabusseau – CIFAR cifar.ca. Checked 21 October 2026.
  5. [2307.01026] Temporal Graph Benchmark for Machine Learning on Temporal Graphs arxiv.org. Checked 21 October 2026.
  6. [2605.16610] Tensor Cookbook: Mastering Tensors through Diagrams arxiv.org. Checked 21 October 2026.