Alexandre Drouin

Fundamental Research Scientist

Element AI

Biography

Coming soon!

Interests

Artificial Intelligence
Machine Learning
Causal Inference
Computational Biology

Education

PhD in Artificial Intelligence, 2019
Laval University
MSc in Artificial Intelligence, 2014
Laval University
BSc in Computer Science, 2012
Laval University

Publications

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Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin (2020). Differentiable Causal Discovery from Interventional Data. ArXiv Preprint.

Philippe Brouillard, Alexandre Drouin, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien (2020). Gradient-based Neural DAG Learning with Interventions. Causal Learning for Decision Making workshop, ICLR 2020.

Pau Rodríguez, Issam Laradji, Alexandre Drouin, Alexandre Lacoste (2020). Embedding Propagation: Smoother Manifold for Few-Shot Classification. European Conference on Computer Vision (ECCV 2020).

Ulysse Côté-Allard, Cheikh Latyr Fall, Alexandre Drouin, Alexandre Campeau-Lecours, Clément Gosselin, Kyrre Glette, François Laviolette, Benoit Gosselin (2019). Deep learning for electromyographic hand gesture signal classification using transfer learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Alexandre Drouin (2019). Inferring phenotypes from genotypes with machine learning: an application to the global problem of antibiotic resistance. PhD Thesis, Université Laval.

Alexandre Drouin, Gaël Letarte, Frédéric Raymond, Mario Marchand, Jacques Corbeil, François Laviolette (2019). Interpretable genotype-to-phenotype classifiers with performance guarantees. Scientific reports.

Francis Brochu, Pier-Luc Plante, Alexandre Drouin, Dominic Gagnon, Dave Richard, Francine Durocher, Caroline Diorio, Mario Marchand, Jacques Corbeil, François Laviolette (2019). Mass spectra alignment using virtual lock-masses. Scientific reports.

Alexandre Drouin, Toby Hocking, François Laviolette (2017). Maximum margin interval trees. Neural Information Processing Systems (NIPS 2017).

Alexandre Drouin, Frédéric Raymond, Gaël Letarte St-Pierre, Mario Marchand, Jacques Corbeil, François Laviolette (2016). Large scale modeling of antimicrobial resistance with interpretable classifiers. Machine Learning for Health Workshop, NIPS.

Alexandre Drouin, Sébastien Giguère, Maxime Déraspe, Mario Marchand, Michael Tyers, Vivian G Loo, Anne-Marie Bourgault, François Laviolette, Jacques Corbeil (2016). Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons. BMC Genomics.

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Talks

La recherche en intelligence artificielle

Nov 27, 2019 12:00 AM Collège Gérald-Godin, Montréal, Canada

Genotype-to-phenotype prediction of antimicrobial resistance

May 2, 2019 12:00 AM AI for Health, Montréal, Canada

Machine learning for antibiotic resistance: from rule-based models to deep architectures

Oct 24, 2017 12:00 AM Argonne National Laboratory, Lemont, Illinois

Maximum Margin Interval Trees

May 24, 2017 12:00 AM Département d'informatique et de génie logiciel, Université Laval, Québec, Canada

Rule-based machine learning algorithms for antibiotic resistance prediction

May 16, 2017 12:00 AM GLBIO 2017, Chicago, Illinois