ServiceNow Research

Alexandre Drouin

Alexandre Drouin

Research Lead

Human Decision Support

Alexandre Drouin is a Research Scientist and the Research Lead of the Human Decision Support Program. He joined ServiceNow Research from Element AI, which he integrated in 2017. He holds a Ph.D. in Computer Science from Université Laval, where he was supervised by François Laviolette. In addition to his position at ServiceNow Research, he is an Adjunct Professor in the Department of Computer Science and Software Engineering at Université Laval and is a member of the scientific committee of Brief.Science. Alexandre’s research interests include algorithms for causal discovery/inference from observational data, practical applications of causal inference, learning robust models from heterogeneous data, and time series forecasting.

Interests
  • Causality
  • Time Series Forecasting
  • Decission Making

Publications

Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts. Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados. At International Conference on Machine Learning (ICML),  2023.

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TACTiS: Transformer-attentional copulas for time series. Alexandre Drouin, Étienne Marcotte, Nicolas Chapados. At Montreal AI Symposium (MAIS),  2022.

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TACTiS: Transformer-Attentional Copulas for Time Series. Alexandre Drouin, Étienne Marcotte, Nicolas Chapados. At International Conference on Machine Learning (ICML),  2022.

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Deconfounding Dynamic Treatment Regimes. David Berger, Alexandre Drouin, Alexandre Lacoste, Laurent Charlin. At SSC,  2022.

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Typing assumptions improve identification in causal discovery - theory and algorithms. Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sébastien Lachapelle, Alexandre Drouin. At Causal Learning and Reasoning (CLeaR),  2022.

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Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark. Alexandre Lacoste, Hannah Kerner, Hamed Alemohammad, Björn Lütjens, Jeremy Irvin, David Dao, Alex Chang, Mehmet Gunturkun, Alexandre Drouin, Pau Rodriguez, David Vazquez, Evan D. Sherwin. At Workshop at the Neural Information Processing Systems (NeurIPS),  2021.

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Typing assumptions improve identification in causal discovery - Report and comments on future directions. Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sébastien Lachapelle, Alexandre Drouin. At Workshop at the Neural Information Processing Systems (NeurIPS),  2021.

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RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data. Thibaud Godon, Pier-Luc Plante, Baptiste Bauvin, Élina Francovic-Fontaine, Alexandre Drouin, François Laviolette, Jacques Corbeil. At Intelligent Systems for Molecular Biology and European Conference on Computational Biology,  2021.

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Typing assumptions improve identification in causal discovery. Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sébastien Lachapelle, Alexandre Drouin. At Workshop at the International Conference on Machine Learning (ICML),  2021.

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Differentiable Causal Discovery from Interventional Data. Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin. At Conference on Neural Information Processing Systems (NeurIPS),  2020.

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In search of robust measures of generalization. Gintare Karolina Dziugaite, Alexandre Drouin, Brayden (Brady) Neal, Nitarshan Rajkumar, Ethan Victor Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy. At Conference on Neural Information Processing Systems (NeurIPS),  2020.

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Synbols: Probing Learning Algorithms with Synthetic Datasets. Alexandre Lacoste, Pau Rodriguez, Frederic Branchaud, Parmida Atighhehchian, Massimo Caccia, Issam H. Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David Vazquez. At Conference on Neural Information Processing Systems (NeurIPS),  2020.

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

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Gradient-Based Neural DAG Learning with Interventions. Philippe Brouillard, Alexandre Drouin, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien. At Workshop at the International Conference on Learning Representations (ICLR),  2020.

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Phylogenetic Manifold Regularization: a semi-supervised approach to predict transcription factor binding sites. Faizy Ahsan, Alexandre Drouin, François Laviolette, Doina Precup, Mathieu Blanchette. At International Conference on Bioinformatics and Biomedicine (BIBM),  2020.

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

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Interpretable genotype-to-phenotype classifiers with performance guarantees. Alexandre Drouin, Gaël Letarte, Frédéric Raymond, Mario Marchand, Jacques Corbeil, François Laviolette. At Nature Scientific Reports,  2018.

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Synbols: Probing Learning Algorithms with Synthetic Datasets. Alexandre Lacoste, Pau Rodriguez, Frédéric Branchaud-Charron, Parmida Atighhehchian, Massimo Caccia, Issam H. Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David Vazquez. At Montreal AI Symposium (MAIS),  2018.

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Deep learning for electromyographic hand gesture signal classification using transfer learning. Ulysse Cote-Allard, Cheikh Latyr Fall, Alexandre Drouin, Alexandre Campeau-Lecours, Clement Gosselin, Kyrre Glette, François Laviolette, Benoit Gosselin. At Transactions on Neural Systems and Rehabilitation Engineering,  2018.

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