Undergraduate Student
Research Assistant @ GRAAL Research Lab
Machine Learning Research Scientist

Université Laval
Pavillon Adrien-Pouliot, Office 3908
1065, avenue de la Médecine
Québec (Québec), Canada G1V 0A6

Supervisor: François Laviolette
Research Groups: Groupe de Recherche en Apprentissage Automatique, Big Data Research Center


Main focus: Learning interpretable models
I seek learning algorithms that produce models that can be refined into knowledge that is valuable to domain experts.
    Phenotype prediction in genomics
      Learning sparse models on extremely high-dimensional data to correlate genomic variations with phenotypes. I collaborate to the development and maintenance of Kover, a tool allowing to learn interpretable computational phenotyping models from k-merized genomic data.
    Develop interpretable rule-based machine learning algorithms
      Develop learning algorithms based on sample-compression theory and take advantage of statistical bounds during the learning phase.
Secondary topics/ Research interests:
  • Interpretable Deep Neural Networks
  • Attention Mechanisms
  • Reinforcement Learning
  • Autonomous driving (Member of the AI division of the VAUL project)

Peer-Reviewed Publications


(coming soon!)

Conferences and Workshops

Drouin, A., Raymond, F., Letarte St-Pierre, G., Marchand, M., Corbeil, J. & Laviolette, F. (2016). Large scale modeling of antimicrobial resistance with interpretable classifiers. Machine Learning for Health Workshop, NIPS, Barcelona, Spain. [ pdf ]


(coming soon!)