Reihaneh Rabbany

hussein.abdallah@mail.concordia.ca

Biographie

Reihaneh Rabbany est professeure adjointe à l'École d'informatique de l'Université McGill. Elle est membre du corps professoral de Mila – Institut québécois d’intelligence artificielle et titulaire d'une chaire en IA Canada-CIFAR. Elle est également membre du corps enseignant du Centre pour l’étude de la citoyenneté démocratique de McGill. Avant de se joindre à l’Université McGill, elle a été boursière postdoctorale à la School of Computer Science de l'Université Carnegie Mellon. Elle a obtenu un doctorat à l’Université de l’Alberta, au Département d'informatique. Elle dirige le laboratoire de données complexes, dont les recherches se situent à l'intersection de la science des réseaux, de l'exploration des données et de l'apprentissage automatique, et se concentrent sur l'analyse des données interconnectées du monde réel et sur les applications sociales.

Étudiants actuels

Hussein Abdallah

Collaborateur·rice de recherche - Concordia

Aurélien Bück-Kaeffer

Maîtrise recherche - McGill

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Doctorat - McGill

Co-superviseur⋅e :

Adriana Romero Soriano

Collaborateur·rice alumni - McGill

Co-superviseur⋅e :

Stagiaire de recherche - McGill

Maîtrise recherche - McGill

Victor Livernoche

Doctorat - McGill

Sitao Luan

Postdoctorat - McGill

Superviseur⋅e principal⋅e :

Guillaume Rabusseau

Shahrad Mohammadzadeh

Maîtrise recherche - McGill

Co-superviseur⋅e :

Collaborateur·rice de recherche - McGill

Maîtrise recherche - McGill

Site web

Soroush Omranpour

Collaborateur·rice alumni - McGill

Co-superviseur⋅e :

Guillaume Rabusseau

kellin.pelrine@mila.quebec

Doctorat - McGill

Site web

Collaborateur·rice alumni - McGill

Collaborateur·rice de recherche

Collaborateur·rice de recherche - McGill University

Sneheel Sarangi

Collaborateur·rice de recherche - McGill

sneheelsarangi@gmail.com

Stagiaire de recherche - McGill

Vidya Sujaya

Maîtrise recherche - McGill

Camille Thibault

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

jacob.mila.handle@tianshome.com

Collaborateur·rice de recherche - McGill

Collaborateur·rice de recherche - UdeM

Superviseur⋅e principal⋅e :

Doctorat - McGill

Jie Zan

Stagiaire de recherche - McGill

jie.zan@mail.mcgill.ca

Sveta Zhuk

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Flight-SEIR : incorporer les données de vol pour améliorer la modélisation épidémiologique et la prévention d’éclosions de maladies infectieuses

Billets de blogue

Flight-SEIR: Incorporating Flight Data to Improve Epidemiological Modelling and Disease Outbreak Prevention

3 août 2021

par

Shenyang Huang

Reihaneh Rabbany

Lire l'article

Publications

A Guide to Misinformation Detection Data and Evaluation

Camille Thibault

Jacob-Junqi Tian

Gabrielle Péloquin-Skulski

James Zhou

Florence Laflamme

Luke Yuxiang Guan

2025-08-03

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)

A Guide to Misinformation Detection Data and Evaluation

Camille Thibault

Jacob-Junqi Tian

Gabrielle Péloquin-Skulski

James Zhou

Florence Laflamme

Yuxiang Guan

Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this probl… (voir plus)em, we have curated the largest collection of (mis)information datasets in the literature, totaling 75. From these, we evaluated the quality of all of the 36 datasets that consist of statements or claims, as well as the 9 datasets that consists of data in purely paragraph form. We assess these datasets to identify those with solid foundations for empirical work and those with flaws that could result in misleading and non-generalizable results, such as insufficient label quality, spurious correlations. We further provide state-of-the-art baselines on all these datasets, but show that regardless of label quality, categorical labels may no longer give an accurate evaluation of detection model performance. We discuss alternatives to mitigate this problem. Overall, this guide aims to provide a roadmap for obtaining higher quality data and conducting more effective evaluations, ultimately improving research in misinformation detection. All datasets and other artifacts are available at [anonymized].

2025-08-03

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)

openreview.net

Responsible AI Day

Ebrahim Bagheri

Faezeh Ensan

Calvin Hillis

Robin Cohen

Benjamin Fung

Sébastien Gambs

2025-08-03

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)

Responsible AI Day

Ebrahim Bagheri

Faezeh Ensan

Calvin Hillis

Robin Cohen

Benjamin Fung

Sébastien Gambs

2025-08-03

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)

Temporal Graph Learning Workshop

Shenyang Huang

Daniele Zambon

Andrea Cini

Julia Gastinger

Micheal Bronstein

2025-08-03

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)

Temporal Graph Learning Workshop

Shenyang Huang

Daniele Zambon

Andrea Cini

Julia Gastinger

Michael Bronstein

2025-08-03

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)

Temporal Graph Learning Workshop

Shenyang Huang

Daniele Zambon

Andrea Cini

Julia Gastinger

Micheal Bronstein

2025-08-03

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)

Hallucination Detox: Sensitivity Dropout (SenD) for Large Language Model Training

Shahrad Mohammadzadeh

Juan David Guerra

Marco Bonizzato

Golnoosh Farnadi

2025-07-01

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)

Veracity: An Open-Source AI Fact-Checking System

Maximilian Puelma Touzel

William Garneau

Manon Gruaz

Mike Pinder

Li Wei Wang

Sukanya Krishna

Luda Cohen

The proliferation of misinformation poses a significant threat to society, exacerbated by the capabilities of generative AI. This demo paper… (voir plus) introduces Veracity, an open-source AI system designed to empower individuals to combat misinformation through transparent and accessible fact-checking. Veracity leverages the synergy between Large Language Models (LLMs) and web retrieval agents to analyze user-submitted claims and provide grounded veracity assessments with intuitive explanations. Key features include multilingual support, numerical scoring of claim veracity, and an interactive interface inspired by familiar messaging applications. This paper will showcase Veracity's ability to not only detect misinformation but also explain its reasoning, fostering media literacy and promoting a more informed society.

2025-06-19

ArXiv (prépublication)

arxiv.org

Veracity: An Open-Source AI Fact-Checking System