Guillaume Rabusseau

Guillaume Rabusseau is an Associate Professor at Mila and the Department of Computer Science and Operations Research (DIRO) at Université de Montréal, holding a Canada CIFAR AI Chair since 2019. His research spans machine learning, theoretical computer science, and multilinear algebra.

• Education: PhD in Computer Science (2016) from Aix-Marseille Université, MSc in Fundamental Computer Science from AMU, BSc in Computer Science (distance learning) from AMU.

Research Interests: Tensor methods for machine learning, spectral learning algorithms, connections between weighted automata, tensor networks, and RNNs, low-rank regression, and nonlinear computational models on structured data.

Publication Trends: Recent work focuses on tensor train decomposition, temporal graph benchmarks, quantum-inspired ML, spectral regularization, and formal methods for sequence modeling. Collaborative papers address dynamic graphs, foundational models for molecular learning, and high-order pooling in GNNs.

Scientific Awards:

• Canada CIFAR AI Chair (2019–present, renewed)

Advising: Supervises PhD students like Maude Lizaire and Pascal Tikeng Notsawo, and MSc students such as Soroush Omranpour. Past advisees include Andy Huang (now at Oxford) and Tianyu Li (Samsung).