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

Jacob Chmura

Master's Research - McGill University

Principal supervisor :

Nicolas Le Roux

jacob.chmura@mila.quebec

Andrea Cini

Independent visiting researcher - Mila

Research Intern - Université de Montréal

mcorriveau303@gmail.com

Johnny Dellas

Research Intern - 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

mattia.greiche@mila.quebec

Mattia Greiche

Research Intern - McGill University

Nazia Hossain

Research Intern - McGill University

nazia.hossain@mila.quebec

Shenyang Huang

Collaborating researcher - McGill University

Co-supervisor :

Anne Imouza

PhD - McGill University

anne.imouza@mila.quebec

Hugh Kelly

Collaborating researcher - McGill University University

hugh.kelly@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

Independent visiting researcher - McGill University

pratheeksha.nair@mila.quebec

Collaborating Alumni - McGill University

sahar.omidishayegan@mila.quebec

farimah.poursafaei@mila.quebec

Farimah Poursafaei

Collaborating Alumni - McGill University

Master's Research - McGill University University

sebastian.sabry@mila.quebec

dorsaf.sallami@mila.quebec

Dorsaf Sallami

Collaborating researcher - McGill University

jacob.mila.handle@tianshome.com

Sneheel Sarangi

Master's Research - McGill University

sneheelsarangi@gmail.com

Collaborating researcher - 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

chin-chen.yang@mila.quebec

Li Wei Wang

Research Intern - McGill University

liweiwang2002@gmail.com

Tong Wu

Collaborating researcher - McGill University University

PhD - McGill University

Shirley Zhao

Collaborating researcher - 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

CrediBench: Building Web-Scale Network Datasets for Information Integrity

James Zhou

Michael M. Bronstein

Shenyang Huang

Online misinformation poses an escalating threat, amplified by the Internet's open nature and increasingly capable LLMs that generate persua… (see more)sive yet deceptive content. Existing misinformation detection methods typically focus on either textual content or network structure in isolation, failing to leverage the rich, dynamic interplay between website content and hyperlink relationships that characterizes real-world misinformation ecosystems. We introduce CrediBench: a large-scale data processing pipeline for constructing temporal web graphs that jointly model textual content and hyperlink structure for misinformation detection. Unlike prior work, our approach captures the dynamic evolution of general misinformation domains, including changes in both content and inter-site references over time. Our processed one-month snapshot extracted from the Common Crawl archive in December 2024 contains 45 million nodes and 1 billion edges, representing the largest web graph dataset made publicly available for misinformation research to date. From our experiments on this graph snapshot, we demonstrate the strength of both structural and webpage content signals for learning credibility scores, which measure source reliability. The pipeline and experimentation code are all available here, and the dataset is in this folder.

2026-08-07

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

ToxiSight: Leveraging Moderator Expertise Through Behavioral Measurement in Gaming Toxicity Annotation

Vicki Chen

Domenico Tullo

Content moderation systems commonly treat human annotators as interchangeable label sources, resolving disagreements through majority voting… (see more) or expert arbitration. We present ToxiSight, an annotation platform that reframes this assumption: rather than extracting consensus, the system supports moderator reasoning by treating hesitation, revision, and disagreement as signals revealing where content is genuinely ambiguous and where taxonomic guidelines fail. ToxiSight integrates gaming-specific contextual widgets with behavioral telemetry, capturing the cognitive processes underlying toxicity validation decisions. Through deployment with 10 professional moderators across 60,000 lines of gaming chat, we demonstrate that behavioral patterns expose systematic category failures invisible to traditional inter-annotator metrics. The Controversial category shows 72% revision rates with fast processing times, indicating immediate recognition of definitional breakdown, while Threats (Life-Threatening) exhibits 75% revisions with slow processing, signaling genuine interpretive complexity. Completion rates improved from 60% to 95%, and moderators reported reduced decision stress when permitted to express uncertainty. This case study demonstrates that trustworthy toxicity detection requires annotation systems designed around the irreducible complexity of human judgment, not against it.

2026-06-28

Canadian Conference on Artificial Intelligence (published)

proceedings.mlr.press

EASE Configuration Facilitates A Reproducible Science of LLM Social Simulations

Sneheel Sarangi

Maximilian Puelma Touzel

Aurélien Bück-Kaeffer

LLMs are increasingly deployed to simulate social interactions, yet many of the existing simulators remain ad hoc and monolithic. This lack … (see more)of architectural standardization prevents reproducible research and complicates downstream evaluation. We advance a rigorous science of LLM-based multi-agent simulation by modularizing core components into Environments, Agents, Simulation engines, and Evaluation metrics (EASE). We demonstrate the utility of EASE configuration by wrapping it in an experimental study schema for orchestrating workflows centered around answering explicit research questions in generated scenarios. We contribute SiliSocS, an open-source, research-ready Silicon Society Sandbox implementing a study-structured EASE configuration to enable highly configurable and reproducible LLM-based social simulations. Using SiliSocS and EASE, we present three case studies, showcasing the system's comprehensive assessment of existing questions, ability to dive deeper into complex questions, and elaboration of existing studies, respectively. Together, these case studies highlight the limitations of current modeling approaches and isolate the impacts of design choices on key results.

2026-05-27

arXiv (preprint)

Hydra: Towards Transferable Multi-Task Learning on Temporal Graphs

Kiarash Shamsi

Tran Gia Bao Ngo

Baris Coskunuzer

Michael M. Bronstein

Cuneyt Gurcan Akcora

Real-world evolving networks are naturally modeled as temporal graphs (TGs), where capturing temporal dynamics is essential for predicting f… (see more)uture graph properties that support downstream decision-making. Existing temporal graph methods have been developed primarily for single-task prediction, and little is known about their generalization across tasks or transfer to unseen networks. This leaves the challenge of multi-task graph property prediction in TGs largely open. We address this challenge by introducing Hydra, a novel architecture that integrates local connectivity features from temporal GNNs with a spectral learning module that captures global connectivity patterns. This design enables joint learning of local and global information under a multi-task objective. In multi-task classification, Hydra achieves an 8.9% relative gain in AUC over the strongest competitor. In multi-task regression, Hydra achieves competitive results in all three tasks, while obtaining the best results in two tasks with a 8.2% relative gain in MAE compared to the strongest baseline. Moreover, Hydra delivers these gains with a 22× reduction in training time compared to temporal transfer models. These results provide the first systematic evidence that multi-task transferable learning on temporal graphs is effective. By delivering consistent top-ranked performance, Hydra highlights multi-task training on temporal graphs as a promising direction toward adaptable foundation models for temporal graphs.

2026-05-20

GFM @ International Conference on Machine Learning (poster)

A systematic review of human-LLM interactions in computational thinking empirical studies

Yimei Zhang

You Song

Doina Precup

Maria Cutumisu

2026-05-11

Computer Science Education (published)

Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection

Victor Livernoche

Jie Zan

Denoising score matching (DSM) provides a way to learn data distributions by training a neural network to recover the score function, define… (see more)d as the gradient of the log density, from noise-corrupted samples. Once trained, the score magnitude at a test point reflects how consistent that point is with the learned distribution, making it a natural anomaly signal. The key practical challenge is selecting the perturbation scale: too little noise yields unstable score estimates in sparse regions, while too much erases local structure and weakens anomaly sensitivity. This is compounded by the difficulty of hyperparameter tuning when anomalies are unknown and no validation set is available. We introduce kurtosis-based noise scaling (K-DSM), a per-feature scheme that sets noise levels from the shape of each marginal distribution, improving coverage of low-density regions and precision in high-density regions without extra model complexity. Contrary to prior claims that multi-scale or noise-conditioned training is necessary, we find that a carefully trained single-scale model is already a strong anomaly detector. On standard tabular anomaly detection benchmarks, K-DSM achieves state-of-the-art performance in the semi-supervised setting. When combined with a lightweight EMA-teacher filtering rule that removes low-density training points before each gradient step, it also achieves strong performance in the fully unsupervised (contaminated) setting, suggesting that simple, data-adaptive noise scaling enables robust anomaly detection while reducing reliance on hyperparameter tuning.

2026-05-06

arXiv (preprint)

The $\textit{Silicon Society}$ Cookbook: Design Space of LLM-based Social Simulations

Aurélien Bück-Kaeffer

Sneheel Sarangi

Maximilian Puelma Touzel

Studies attempting to simulate human behavior with …

2026-04-29

arXiv (preprint)

Deepfakes in the 2025 Canadian Election: Prevalence, Partisanship, and Platform Dynamics

Victor Livernoche

Andreea Musulan

Concerns about AI-generated political content are growing, yet there is limited empirical evidence on how deepfakes actually appear and circ… (see more)ulate across social platforms during major events in democratic countries. In this study, we present one of the first in-depth analyses of how these realistic synthetic media shape the political landscape online, focusing specifically on the 2025 Canadian federal election. By analyzing 187,778 posts from X, Bluesky, and Reddit with a high-accuracy detection framework trained on a diverse set of modern generative models, we find that 5.86% of election-related images were deepfakes. Right-leaning accounts shared them more frequently, with 8.66% of their posted images flagged compared to 4.42% for left-leaning users, often with defamatory or conspiratorial intent. Yet, most detected deepfakes were benign or non-political, and harmful ones drew little attention, accounting for only 0.12% of all views on X. Overall, deepfakes were present in the election conversation, but their reach was modest, and realistic fabricated images, although less common, drew higher engagement, highlighting growing concerns about their potential misuse.

2026-04-11

ACM Web Conference (published)

AIF-GEN: Open-Source Platform and Synthetic Dataset Suite for Reinforcement Learning on Large Language Models

Jacob Chmura

Shahrad Mohammadzadeh

Taz Scott-Talib

Nishanth Anand

2026-03-01

LLA @ International Conference on Learning Representations (poster)

What do people want to fact-check?

Bijean Ghafouri

Dorsaf Sallami

Luca Luceri

Taylor Lynn Curtis

Emilio Ferrara

2026-02-10

arXiv (preprint)

Grounding Computer Use Agents on Human Demonstrations

Aarash Feizi

Shravan Nayak

Xiangru Jian

Kevin Qinghong Lin

Kaixin Li

Rabiul Awal

Xing Han Lu

Johan Obando-Ceron

Juan A. Rodriguez

Nicolas Chapados

David Vázquez

Adriana Romero-Soriano

Perouz Taslakian

Christopher Pal

Spandana Gella

Sai Rajeswar

Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen eleme… (see more)nts. While large datasets exist for web and mobile interactions, high-quality resources for desktop environments are limited. To address this gap, we introduce GroundCUA, a large-scale desktop grounding dataset built from expert human demonstrations. It covers 87 applications across 12 categories and includes 56K screenshots, with every on-screen element carefully annotated for a total of over 3.56M human-verified annotations. From these demonstrations, we generate diverse instructions that capture a wide range of real-world tasks, providing high-quality data for model training. Using GroundCUA, we develop the GroundNext family of models that map instructions to their target UI elements. At both 3B and 7B scales, GroundNext achieves state-of-the-art results across five benchmarks using supervised fine-tuning, while requiring less than one-tenth the training data of prior work. Reinforcement learning post-training further improves performance, and when evaluated in an agentic setting on the OSWorld benchmark using o3 as planner, GroundNext attains comparable or superior results to models trained with substantially more data,. These results demonstrate the critical role of high-quality, expert-driven datasets in advancing general-purpose computer-use agents.

2026-01-25

International Conference on Learning Representations (poster)

ControBench: An Interaction-Aware Benchmark for Controversial Discourse Analysis on Social Networks

Ta Thanh Thuy

Jiaqi Zhu

Xuan Liu

Lin Shang

Guillaume Rabusseau

Lihui Chen

Zheng Yilun

Sitao Luan

Understanding how people argue across ideological divides online is important for studying political polarization, misinformation, and conte… (see more)nt moderation. Existing datasets capture only part of this problem: some preserve text but ignore interaction structure, some model structure without rich semantics, and others represent conversations without stable user-level ideological identity. We introduce ControBench, a benchmark for controversial discourse analysis that combines heterogeneous social interaction graphs with rich textual semantics. Built from Reddit discussions on three topics, Trump, abortion, and religion, ControBench contains 7,370 users, 1,783 posts, and 26,525 interactions. The graph contains user and post nodes connected by semantically enriched edges; in particular, user-comment-user edges encode both a reply and the parent comment that it responds to, preserving local argumentative context. User labels are derived from self-declared Reddit flairs, providing a scalable proxy for ideological identity without manual annotation. The resulting datasets exhibit low or negative adjusted homophily (Trump: -0.77, Abortion: 0.06, Religion: 0.04), reflecting the cross-cutting structure of real-world debate. We evaluate graph neural networks, pretrained language models, and large language models on ControBench and observe distinct performance patterns across topics and model families, especially when ideological boundaries are ambiguous. These results position ControBench as a challenging and realistic benchmark for controversial discourse analysis.

2025-12-31

Unpublished (published)