Artificial Intelligence

Multidisciplinary Research

In the field of Artificial Intelligence, my team is interested in Statistical Learning, Information Retrieval, Natural Language Processing and Data Mining. Most of our research projects are multidisciplinary, and rely on deep learning and/or information retrieval approaches.

Some Projects


- Risk detection, application to mental health: Prediction from user-generated content in social media. Participation in evaluation campaigns like CLPsych and eRisk.

- SylvCiT: An artificial intelligence based tool designed to optimize the resilience of urban and peri-urban forests in North America. It aims to provide practical recommendations and analysis to improve the health, diversity and ecosystem services provided by urban forest.


- Artificial intelligence, ethics, justice and human rights (collaboration with H. Cyr, Dean of the Faculty of Political Science and Law, UQAM, and S. Gambs, PhD, Computer Science, UQAM). More details here: LegalIA and there HumanIA

- Predictive justice: prediction of case outcome, application to Federal Court decisions.

- Academic data analysis (collaboration with M. Bouguessa, PhD, Computer Science, UQAM): data modelling, profile extraction, success prediction.

- Mining, analysis and classification of professional communications (emails, forum posts, etc.) towards sensitive data detection (industrial collaboration).

- Probabilistic models for spoken language interpretation in human/machine dialogs.

- Collection and mining of big data from users of massively multiplayer online video games (collaboration with M. Bonenfant, PhD, Communication, UQAM).

- Genomics: detection of secondary metabolites using deep learning (collaboration with A. Tsang, PhD, Director of the Centre for Structural and Functional Genomics, Concordia University).

- Graph theory and application to linked data and information retrieval.