Reihaneh Rabbany

hussein.abdallah@mail.concordia.ca

Biography

Reihaneh Rabbany is an assistant professor at the School of Computer Science, McGill University, and a core academic member of Mila – Quebec Artificial Intelligence Institute. She is also a Canada CIFAR AI Chair and on the faculty of McGill’s Centre for the Study of Democratic Citizenship.

Before joining McGill, Rabbany was a postdoctoral fellow at the School of Computer Science, Carnegie Mellon University. She completed her PhD in the Department of Computing Science at the University of Alberta.

Rabbany heads McGill’s Complex Data Lab, where she conducts research at the intersection of network science, data mining and machine learning, with a focus on analyzing real-world interconnected data and social good applications.

Current Students

Hussein Abdallah

Postdoctorate - McGill University

oumaima.amezgar@mila.quebec

Oumaima Amezgar

PhD - Université de Montréal

Aurélien Bück-Kaeffer

Master's Research - McGill University

aurelien.buck-kaeffer@mila.quebec

Andrea Cini

Independent visiting researcher - Mila

Research Intern - Université de Montréal

mcorriveau303@gmail.com

Johnny Dellas

PhD - McGill University

john.dellas@mila.quebec

Islam Eldifrawi

Independent visiting researcher - University of Sherbrooke

sulum821991@gmail.com

sophia.garrel@mila.quebec

Aarash Feizi

PhD - McGill University

Co-supervisor :

Adriana Romero Soriano

Collaborating researcher - McGill University

nicholas.gaudet@mila.quebec

Nicholas Gaudet

PhD - Université de Montréal

mattia.greiche@mila.quebec

Mattia Greiche

Research Intern - McGill University

Nazia Hossain

Research Intern - McGill University University

nazia.hossain@mila.quebec

Anne Imouza

PhD - McGill University

anne.imouza@mila.quebec

Emma Kondrup

PhD - McGill University

Co-supervisor :

Catherine Régis

emma.kondrup@mila.quebec

Andrew Lin

Research Intern - McGill University University

andrew.lin@mila.quebec

victor.livernoche@mila.quebec

Victor Livernoche

PhD - McGill University

shahrad.mohammadzadeh@mila.quebec

Sitao Luan

Postdoctorate - McGill University

Principal supervisor :

Shahrad Mohammadzadeh

Master's Research - McGill University

Co-supervisor :

Doina Precup

Collaborating Alumni - McGill University

farimah.poursafaei@mila.quebec

PhD - Université de Montréal

meissa.rebbani@mila.quebec

sebastian.sabry@mila.quebec

Sebastian Sabry

Master's Research - McGill University University

dorsaf.sallami@mila.quebec

Dorsaf Sallami

Independent visiting researcher - McGill University

jacob.mila.handle@tianshome.com

Sneheel Sarangi

Master's Research - McGill University

sneheelsarangi@gmail.com

Master's Research - McGill University University

shiyan.wliu@gmail.com

Jacob-Junqi Tian

Master's Research - McGill University

Collaborating researcher - Université de Montréal

Principal supervisor :

Guillaume Rabusseau

aaron.tjandra@mila.quebec

Li Wei Wang

Research Intern - McGill University

liweiwang2002@gmail.com

Tong Wu

Collaborating researcher - McGill University University

tong.wu@mila.quebec

chin-chen.yang@mila.quebec

Brian Yang

PhD - McGill University

brian.yang@mila.quebec

PhD - McGill University

Shirley Zhao

Master's Research - McGill University

xueli.zhao@mila.quebec

svetlana.zhuk@mila.quebec

Sveta Zhuk

Master's Research - Université de Montréal

Blog Posts

Un groupe hétéroclite de huit jeunes adultes se tient serré sur un toit, souriant et riant avec la silhouette de la ville en arrière-plan. Deux encarts circulaires mettent en évidence une caméra vintage tenue par l'un des membres du groupe, soulignant l'élément deepfake.

December 16, 2025

Unmasking deepfakes with AI

Victor Livernoche

Reihaneh Rabbany

Read the article

August 3, 2021

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

Shenyang Huang

Reihaneh Rabbany

Read the article

Publications

SandboxSocial: A Sandbox for Social Media Using Multimodal AI Agents

Maximilian Puelma Touzel

Sneheel Sarangi

Gayatri Krishnakumar

Busra Tugce Gurbuz

Austin Welch

Zachary Yang

Andreea Musulan

Hao Yu

Ethan Kosak-Hine

Tom Gibbs

Camille Thibault

Dan Zhao

The online information ecosystem enables influence campaigns of unprecedented scale and impact. We urgently need empirically grounded approa… (see more)ches to counter the growing threat of malicious campaigns, now amplified by generative AI. But, developing defenses in real-world settings is impractical. Social system simulations with agents modelled using Large Language Models (LLMs) are a promising alternative approach and a growing area of research. However, existing simulators lack features needed to capture the complex information-sharing dynamics of platform-based social networks. To bridge this gap, we present SandboxSocial, a new simulator that includes several key innovations, mainly: (1) a virtual social media platform (modelled as Mastodon and mirrored in an actual Mastodon server) that enables a realistic setting in which agents interact; (2) an adapter that uses real-world user data to create more grounded agents and social media content; and (3) multi-modal capabilities that enable our agents to interact using both text and images---just as humans do on social media. We make the simulator more useful to researchers by providing measurement and analysis tools that track simulation dynamics and compute evaluation metrics to compare experimental results.

2025-08-15

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (published)

Veracity: An Open-Source AI Fact-Checking System

Taylor Lynn Curtis

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… (see more) 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.

2025-08-15

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (published)

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

Simeon Campos

Duncan Cass-Beggs … (see 68 more)

Jiahao Chen

Rumman Chowdhury

Kuan Chua Seah

Jeff Clune

Juntao Dai

Agnes Delaborde

Nouha Dziri

Francisco Eiras

Joshua Engels

Jinyu Fan

Adam Gleave

Noah Goodman

Fynn Heide

Johannes Heidecke

Dan Hendrycks

Cyrus Hodes

Bryan Low Kian Hsiang

Minlie Huang

Sami Jawhar

Wang Jingyu

Adam Tauman Kalai

Meindert Kamphuis

Mohan 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

Seán Ó hÉigeartaigh

Alejandro Ortega

Pierre Peigné

James Petrie

Benjamin Prud'homme

Nayat Sanchez-Pi

Sarah Schwettmann

Buck Shlegeris

Saad Siddiqui

Aradhana Sinha

Martín Soto

Cheston Tan

Dong Ting

William Tjhi

Robert Trager

Brian Tse

Anthony Tung K. H.

Vanessa Wilfred

John Willes

Denise Wong

Wei Xu

Rongwu Xu

Yi Zeng

HongJiang Zhang

Djordje Žikelić

Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to en… (see more)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.

2025-08-10

SuperIntelligence - Robotics - Safety & Alignment (published)

A Guide to Misinformation Detection Data and Evaluation

Camille Thibault

Jacob-Junqi Tian

Gabrielle Peloquin-Skulski

Taylor Lynn Curtis

James Zhou

Florence Laflamme

Luke Yuxiang Guan

Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this, we h… (see more)ave curated the largest collection of (mis)information datasets in the literature, totaling 75. From these, we evaluated the quality of 36 datasets that consist of statements or claims, as well as the 9 datasets that consist 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 spurious correlations, or examples that are ambiguous or otherwise impossible to assess for veracity. We find the latter issue is particularly severe and affects most datasets in the literature. 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. Finally, we propose and highlight Evaluation Quality Assurance (EQA) as a tool to guide the field toward systemic solutions rather than inadvertently propagating issues in evaluation. Overall, this guide aims to provide a roadmap for higher quality data and better grounded evaluations, ultimately improving research in misinformation detection. All datasets and other artifacts are available at https://misinfo-datasets.complexdatalab.com/.

2025-08-02

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (published)

Responsible AI Day

Ebrahim Bagheri

Faezeh Ensan

Calvin Hillis

Robin Cohen

Benjamin C. M. Fung

Sébastien Gambs

This special day event on Responsible Artificial Intelligence (AI) brings together researchers, practitioners, and policymakers to explore h… (see more)ow data mining and machine learning systems can be designed to align with ethical principles, societal values, and human well-being. As AI technologies increasingly influence decisions in healthcare, finance, governance, and social systems, there is a critical need to develop frameworks that embed fairness, accountability, and privacy directly into the foundations of knowledge discovery. This full-day event will feature a mix of invited talks, interactive debates, expert panels, and peer-reviewed research presentations, all focused on the practical integration of ethical design into data-driven systems. The Responsible AI Day builds on the success of Canada's NSERC CREATE Program on Responsible AI, an interdisciplinary initiative training the next generation of AI researchers across computer science, law, bioethics, public health, and media studies. Topics will span scalable AI governance, privacy-preserving computation, algorithmic bias mitigation, and the socio-legal tensions emerging in generative AI. By positioning responsible AI as a sociotechnical challenge, this special day aligns with KDD's mission of advancing data science that is not only technically robust but also socially conscious.

2025-08-02

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

Temporal Graph Learning Workshop

Daniele Zambon

Michael M. Bronstein

2025-08-02

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (published)

Unified Game Moderation: Soft-Prompting and LLM-Assisted Label Transfer for Resource-Efficient Toxicity Detection

Zachary Yang

Domenico Tullo

Toxicity detection in gaming communities faces significant scaling challenges when expanding across multiple games and languages, particular… (see more)ly in real-time environments where computational efficiency is crucial. We present two key findings to address these challenges while building upon our previous work on ToxBuster, a BERT-based real-time toxicity detection system. First, we introduce a soft-prompting approach that enables a single model to effectively handle multiple games by incorporating game-context tokens, matching the performance of more complex methods like curriculum learning while offering superior scalability. Second, we develop an LLM-assisted label transfer framework using GPT-4o-mini to extend support to seven additional languages. Evaluations on real game chat data across French, German, Portuguese, and Russian achieve macro F1-scores ranging from 32.96% to 58.88%, with particularly strong performance in German, surpassing the English benchmark of 45.39%. In production, this unified approach significantly reduces computational resources and maintenance overhead compared to maintaining separate models for each game and language combination. At Ubisoft, this model successfully identifies an average of 50 players, per game, per day engaging in sanctionable behavior.

2025-08-02

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (published)

Uncovering Hidden Factions through Text-Network Representations: Unsupervised Public Opinion Mapping of Iran on Twitter in the 2022 Unrest

Sahar Omidi Shayegan

Ideological mapping on social media is typically framed as a supervised classification task that depends on stable party systems and abundan… (see more)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.

2025-07-25

colmweb.org/COLM/2025/Workshop/NLPOR (published)

TRUTH: Teaching LLMs to Rerank for Truth in Misinformation Detection

Hao Yu

Shenyang Huang

Zachary Yang

Maximilian Puelma Touzel

2025-07-24

colmweb.org/COLM/2025/Workshop/SoLaR (poster)

A Systematic Literature Review of Large Language Model Applications in the Algebra Domain

Yajie Song

Yimei Zhang

Doina Precup

Maria Cutumisu

2025-06-09

Proceedings (International Conference of the Learning Sciences) (published)

TGM: A Modular Framework for Machine Learning on Temporal Graphs

Michael M. Bronstein

Matthias Fey

While deep learning on static graphs has been revolutionized by standardized libraries like PyTorch Geometric and DGL, machine learning on T… (see more)emporal Graphs (TG), networks that evolve over time, lacks comparable software infrastructure. Existing TG libraries are limited in scope, focusing on a single method category or specific algorithms. We introduce Temporal Graph Modelling (TGM), a comprehensive framework for machine learning on temporal graphs to address this gap. Through a modular architecture, TGM is the first library to support both discrete and continuous-time TG methods and implements a wide range of TG methods. The TGM framework combines an intuitive front-end API with an optimized backend storage, enabling reproducible research and efficient experimentation at scale. Key features include graph-level optimizations for offline training and built-in performance profiling capabilities. Through extensive benchmarking on five real-world networks, TGM is up to 6 times faster than the widely used DyGLib library on TGN and TGAT models and up to 8 times faster than the UTG framework for converting edges into coarse-grained snapshots.

2025-06-08

ICML.cc/2025/Workshop/CODEML (published)

Weak Supervision for Real World Graphs

Pratheeksha Nair

2025-06-02

ArXiv (preprint)