Alexandre Drouin

Staff Research Scientist



Welcome to my page!

I’m a Staff Research Scientist at ServiceNow Research in Montreal and an Adjunct Professor of Computer Science at Laval University. I lead ServiceNow’s Human Decision Support research program, which explores the use of machine learning for decision-making in complex dynamic environments.

My main research interest is causal decision-making under uncertainty, where the goal is to answer questions of causal nature (interventions, counterfactual), while accounting for sources of uncertainty, such as ambiguity in causal structures and unmeasured variables. I am also interested in probabilistic time series forecasting and its use to foresee the long-term effect of actions.

In the past, I developed machine learning algorithms for biomarker discovery in large genomic datasets and focussed on applications to the global problem of antibiotic resistance. I obtained my Ph.D. in 2019 under the supervision of François Laviolette.


  • Machine learning
  • Causal inference and discovery
  • Time series forecasting
  • Model robustness


  • PhD in Artificial Intelligence, 2019

    Laval University

  • MSc in Artificial Intelligence, 2014

    Laval University

  • BSc in Computer Science, 2012

    Laval University


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(2022). TACTiS: Transformer-Attentional Copulas for Time Series. 39th International Conference on Machine Learning (ICML 2022).

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(2022). Typing Assumptions Improve Identification in Causal Discovery. 1st Conference on Causal Learning and Reasoning (CLeaR 2022).

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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.


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

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Differentiable Causal Discovery with Observational and Interventional Data

Knowledge of the causal structure that underlies a data generating process is essential to answering questions of causal nature. Such …

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