Portrait of Bruno Rousseau

Bruno Rousseau

bruno.rousseau@mila.quebec
Senior Applied Research Scientist, Applied Machine Learning Research

Publications

Large scale Raman spectrum calculations in defective 2D materials using deep learning
Olivier Malenfant-Thuot
Dounia Shaaban Kabakibo
Simon Blackburn
Bruno Rousseau
Michel Côté
We introduce a machine learning prediction workflow to study the impact of defects on the Raman response of 2D materials. By combining the u… (see more)se of machine-learned interatomic potentials, the Raman-active Γ-weighted density of states method and splitting configurations in independant patches, we are able to reach simulation sizes in the tens of thousands of atoms, with diagonalization now being the main bottleneck of the simulation. We apply the method to two systems, isotopic graphene and defective hexagonal boron nitride, and compare our predicted Raman response to experimental results, with good agreement. Our method opens up many possibilities for future studies of Raman response in solid-state physics.
2024-12-17
Journal of Physics: Condensed Matter (published)
doi.org
arxiv.org
A deep learning benchmark for first break detection from hardrock seismic reflection data
Pierre-Luc St-Charles
Bruno Rousseau
Joumana Ghosn
Gilles Bellefleur
Ernst Schetselaar
Deep learning techniques are used to tackle a variety of tasks related to seismic data processing and interpretation. Although many works ha… (see more)ve shown the benefits of deep learning, assessing the generalization capabilities of proposed methods for data acquired in different conditions and geologic environments remains challenging. This is especially true for applications in hardrock environments. The primary factors that impede the adoption of machine learning in geosciences include the lack of publicly available and labeled data sets and the use of inadequate evaluation methodologies. Because machine learning models are prone to overfit and underperform when the data used to train them are site specific, the applicability of these models on new survey data that could be considered “out-of-distribution” is rarely addressed. This is unfortunate, as evaluating predictive models in out-of-distribution settings can provide a good insight into their usefulness in real-world use cases. To tackle these issues, we develop a simple benchmarking methodology for first break picking to evaluate the transferability of deep learning models that are trained across different environments and acquisition conditions. For this, we consider a reflection seismic survey data set acquired at five distinct hardrock mining sites combined with annotations for first break picking. We train and evaluate a baseline deep learning solution based on a U-Net for future comparisons and discuss potential improvements to this approach.
2023-10-09
Geophysics (published)
doi.org