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Alexandre Drouin

Fundamental Research Scientist

Element AI

Biography

I am a Fundamental Research Scientist at Element AI in Montreal, Canada. My current research is at the intersection of causal inference and machine learning. I am particularly interested in methods for causal discovery, which I believe is a problem of fundamental interest in science.

I obtained my PhD in machine learning from Laval University, under the supervision of François Laviolette, Mario Marchand, and Jacques Corbeil. In my thesis, I proposed machine learning algorithms with theoretical guarantees to uncover genotype-to-phenotype associations in large genomic datasets, and showed how these could be used in addressing the global problem of antibiotic resistance.

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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(2020). Synbols: Probing Learning Algorithms with Synthetic Datasets. Neural Information Processing Systems (NeurIPS 2020).

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(2020). Differentiable Causal Discovery from Interventional Data. Neural Information Processing Systems (NeurIPS 2020).

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(2020). Gradient-based Neural DAG Learning with Interventions. Causal Learning for Decision Making workshop, ICLR 2020.

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(2020). Embedding Propagation: Smoother Manifold for Few-Shot Classification. European Conference on Computer Vision (ECCV 2020).

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(2019). Deep learning for electromyographic hand gesture signal classification using transfer learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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(2019). Interpretable genotype-to-phenotype classifiers with performance guarantees. Scientific reports.

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(2017). Maximum margin interval trees. Neural Information Processing Systems (NIPS 2017).

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Talks

La recherche en intelligence artificielle

Genotype-to-phenotype prediction of antimicrobial resistance

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

Maximum Margin Interval Trees