Portrait de Reihaneh Rabbany

Reihaneh Rabbany

Membre académique principal
reihaneh.rabbany@mila.quebec
Chaire en IA Canada-CIFAR
Professeure adjointe, McGill University, École d'informatique
Sujets de recherche
Apprentissage de représentations
Apprentissage sur graphes
Exploration des données
Réseaux de neurones en graphes
Traitement du langage naturel
Site web
Google Scholar
LinkedIn

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
Github
Aurélien Bück-Kaeffer
Maîtrise recherche - McGill
Github
Jacob Chmura
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e :
Nicolas Le Roux
Site web
Github
Aarash Feizi
Doctorat - McGill
Co-superviseur⋅e :
Adriana Romero Soriano
Site web
Github
Google Scholar
Shenyang Huang
Collaborateur·rice alumni - McGill
Co-superviseur⋅e :
Guillaume Rabusseau
Site web
Github
Google Scholar
Emma Kondrup
Stagiaire de recherche - McGill
Site web
Github
Google Scholar
Victor Livernoche
Doctorat - McGill
Site web
Github
Google Scholar
Sitao Luan
Postdoctorat - McGill
Superviseur⋅e principal⋅e :
Guillaume Rabusseau
Github
Google Scholar
Shahrad Mohammadzadeh
Maîtrise recherche - McGill
Co-superviseur⋅e :
Doina Precup
Site web
Github
Google Scholar
Pratheeksha Nair
Collaborateur·rice de recherche - McGill
Site web
Github
Google Scholar
Sahar Omidi Shayegan
Maîtrise recherche - McGill
Site web
Github
Soroush Omranpour
Collaborateur·rice de recherche - McGill
Co-superviseur⋅e :
Guillaume Rabusseau
Github
Farimah Poursafaei
Collaborateur·rice alumni - McGill
Site web
Github
Google Scholar
Sebastian Sabry
Collaborateur·rice de recherche - McGill University
Github
Sneheel Sarangi
Collaborateur·rice de recherche - McGill
Site web
Github
Google Scholar
Camille Thibault
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Jean-François Godbout
Site web
Github
Google Scholar
Jacob-Junqi Tian
Collaborateur·rice de recherche - McGill
Site web
Github
Google Scholar
Aaron Tjandra
Collaborateur·rice de recherche - UdeM
Superviseur⋅e principal⋅e :
Guillaume Rabusseau
Site web
Google Scholar
Zachary Yang
Doctorat - McGill
Github
Google Scholar
Jie Zan
Stagiaire de recherche - McGill
Sveta Zhuk
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Jean-François Godbout

Publications

A Guide to Misinformation Detection Data and Evaluation
Camille Thibault
Jacob-Junqi Tian
Gabrielle Péloquin-Skulski
Taylor Lynn Curtis
James Zhou
Florence Laflamme
Yuxiang Guan
Reihaneh Rabbany
Jean-François Godbout
Kellin Pelrine
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)
doi.org
openreview.net
Responsible AI Day
Ebrahim Bagheri
Faezeh Ensan
Calvin Hillis
Reihaneh Rabbany
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)
doi.org
Responsible AI Day
Ebrahim Bagheri
Faezeh Ensan
Calvin Hillis
Reihaneh Rabbany
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)
doi.org
Temporal Graph Learning Workshop
Shenyang Huang
Daniele Zambon
Andrea Cini
Farimah Poursafaei
Jacob Chmura
Julia Gastinger
Reihaneh Rabbany
Micheal Bronstein
2025-08-03
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)
doi.org
Temporal Graph Learning Workshop
Shenyang Huang
Daniele Zambon
Andrea Cini
Farimah Poursafaei
Jacob Chmura
Julia Gastinger
Reihaneh Rabbany
Micheal Bronstein
2025-08-03
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)
doi.org
Temporal Graph Learning Workshop
Shenyang Huang
Daniele Zambon
Andrea Cini
Farimah Poursafaei
Jacob Chmura
Julia Gastinger
Reihaneh Rabbany
Michael Bronstein
2025-08-03
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)
doi.org
Uncovering Hidden Factions through Text-Network Representations: Unsupervised Public Opinion Mapping of Iran on Twitter in the 2022 Unrest
Sahar Omidi Shayegan
Jean-François Godbout
Reihaneh Rabbany
Ideological mapping on social media is typically framed as a supervised classification task that depends on stable party systems and abundan… (voir plus)t annotated data. These assumptions fail in contexts with weak political institutionalization, such as Iran. We recast ideology detection as a fully unsupervised mapping problem and introduce a text-network representation system, uncovering latent ideological factions on Persian Twitter during the 2022 Mahsa Amini protests. Using hundreds of millions of Persian tweets, we learn joint text–network embeddings by fine-tuning ParsBERT with a combined masked-language-modeling and contrastive objective and by passing the embeddings through a Graph Attention Network trained for link prediction on time-batched subgraphs. The pipeline integrates semantic and structural signals without observing labels. Density-based clustering reveals eight ideological blocs whose spatial relations mirror known political alliances. Alignment with 883 expert-labeled accounts yields 53% accuracy. This label-free framework scales to label-scarce contexts, offering new leverage for studying political debates online.
2025-07-26
colmweb.org/COLM/2025/Workshop/NLPOR (publié)
openreview.net
openreview.net
Uncovering Hidden Factions through Text-Network Representations: Unsupervised Public Opinion Mapping of Iran on Twitter in the 2022 Unrest
Sahar Omidi Shayegan
Jean-François Godbout
Reihaneh Rabbany
Ideological mapping on social media is typically framed as a supervised classification task that depends on stable party systems and abundan… (voir plus)t annotated data. These assumptions fail in contexts with weak political institutionalization, such as Iran. We recast ideology detection as a fully unsupervised mapping problem and introduce a text-network representation system, uncovering latent ideological factions on Persian Twitter during the 2022 Mahsa Amini protests. Using hundreds of millions of Persian tweets, we learn joint text–network embeddings by fine-tuning ParsBERT with a combined masked-language-modeling and contrastive objective and by passing the embeddings through a Graph Attention Network trained for link prediction on time-batched subgraphs. The pipeline integrates semantic and structural signals without observing labels. Density-based clustering reveals eight ideological blocs whose spatial relations mirror known political alliances. Alignment with 883 expert-labeled accounts yields 53% accuracy. This label-free framework scales to label-scarce contexts, offering new leverage for studying political debates online.
2025-07-26
colmweb.org/COLM/2025/Workshop/NLPOR (publié)
openreview.net
openreview.net
TRUTH: Teaching LLMs to Rerank for Truth in Misinformation Detection
Hao Yu
Shenyang Huang
Zachary Yang
Maximilian Puelma Touzel
Kellin Pelrine
Jean-François Godbout
Reihaneh Rabbany
2025-07-25
colmweb.org/COLM/2025/Workshop/SoLaR (poster)
openreview.net
openreview.net
TRUTH: Teaching LLMs to Rerank for Truth in Misinformation Detection
Hao Yu
Shenyang Huang
Zachary Yang
Maximilian Puelma Touzel
Kellin Pelrine
Jean-François Godbout
Reihaneh Rabbany
Misinformation detection presents a significant challenge due to its knowledge-intensive and reasoning-intensive nature. While Retrieval-Aug… (voir plus)mented Generation (RAG) systems offer a promising direction, the effectiveness of their retrieval and reranking components is crucial. This paper introduces TRUTH, a novel reranking approach designed for domain adaptation, specifically for misinformation detection, which employs a two-stage training methodology: Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). We demonstrate that our 1B parameter TRUTH model achieves strong performance comparable to 7B models on established misinformation benchmarks such as FEVER and Canadian bilingual news datasets, improving retrieval quality and positively impacting downstream task accuracy. Our findings highlight the efficacy of combining SFT for broad knowledge acquisition and domain adaptation with DPO for nuanced reasoning alignment in developing efficient and effective rerankers for complex, knowledge-intensive tasks. Datasets and code will be available with the camera-ready version of the paper.
2025-07-25
colmweb.org/COLM/2025/Workshop/SoLaR (poster)
openreview.net
openreview.net
Hallucination Detox: Sensitivity Dropout (SenD) for Large Language Model Training
Shahrad Mohammadzadeh
Juan David Guerra
Marco Bonizzato
Reihaneh Rabbany
Golnoosh Farnadi
2025-07-01
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)
doi.org
The Singapore Consensus on Global AI Safety Research Priorities
Yoshua Bengio
Tegan Maharaj
Luke Ong
Stuart Russell
Dawn Song
Max Tegmark
Lan Xue
Ya-Qin Zhang
Stephen Casper
Wan Sie Lee
Sören Mindermann
Vanessa Wilfred
Vidhisha Balachandran
Fazl Barez
Michael Belinsky
Imane Bello
Malo Bourgon
Mark Brakel
Sim'eon Campos
Duncan Cass-Beggs … (voir 67 de plus)
Jiahao Chen
Rumman Chowdhury
Kuan Chua Seah
Jeff Clune
Juntao Dai
Agnès Delaborde
Nouha Dziri
Francisco Eiras
Joshua Engels
Jinyu Fan
Adam Gleave
Noah D. Goodman
Fynn Heide
Johannes Heidecke
Dan Hendrycks
Cyrus Hodes
Bryan Low Kian Hsiang
Minlie Huang
Sami Jawhar
Jingyu Wang
Adam Tauman Kalai
Meindert Kamphuis
Mohan S. Kankanhalli
Subhash Kantamneni
Mathias Bonde Kirk
Thomas Kwa
Jeffrey Ladish
Kwok-Yan Lam
Wan Lee Sie
Taewhi Lee
Xiaojian Li
Jiajun Liu
Chaochao Lu
Yifan Mai
Richard Mallah
Julian Michael
Nick Moës
Simon Möller
Kihyuk Nam
Kwan Yee Ng
Mark Nitzberg
Besmira Nushi
Sean O hEigeartaigh
Alejandro Ortega
Pierre Peigné
James Petrie
Benjamin Prud'homme
Reihaneh Rabbany
Nayat Sanchez-Pi
Sarah Schwettmann
Buck Shlegeris
Saad Siddiqui
Aradhana Sinha
Martín Soto
Cheston Tan
Dong Ting
William-Chandra Tjhi
Robert Trager
Brian Tse
H. AnthonyTungK.
John Willes
Denise Wong
W. Xu
Rongwu Xu
Yi Zeng
HongJiang Zhang
Djordje Zikelic
Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to en… (voir plus)sure that AI is safe, i.e., trustworthy, reliable, and secure. Building a trusted ecosystem is therefore essential – it helps people embrace AI with confidence and gives maximal space for innovation while avoiding backlash. This requires policymakers, industry, researchers and the broader public to collectively work toward securing positive outcomes from AI’s development. AI safety research is a key dimension. Given that the state of science today for building trustworthy AI does not fully cover all risks, accelerated investment in research is required to keep pace with commercially driven growth in system capabilities. Goals: The 2025 Singapore Conference on AI (SCAI): International Scientific Exchange on AI Safety aims to support research in this important space by bringing together AI scientists across geographies to identify and synthesise research priorities in AI safety. The result, The Singapore Consensus on Global AI Safety Research Priorities, builds on the International AI Safety Report-A (IAISR) chaired by Yoshua Bengio and backed by 33 governments. By adopting a defence-in-depth model, this document organises AI safety research domains into three types: challenges with creating trustworthy AI systems (Development), challenges with evaluating their risks (Assessment), and challenges with monitoring and intervening after deployment (Control). Through the Singapore Consensus, we hope to globally facilitate meaningful conversations between AI scientists and AI policymakers for maximally beneficial outcomes. Our goal is to enable more impactful R&D efforts to rapidly develop safety and evaluation mechanisms and foster a trusted ecosystem where AI is harnessed for the public good.
2025-06-25
ArXiv (prépublication)
doi.org
arxiv.org