Reihaneh Rabbany

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

Postdoctorat - McGill

Taimaa Bachi

Stagiaire de recherche - McGill

Maîtrise recherche - McGill

Jacob Chmura

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Stagiaire de recherche - UdeM

Superviseur⋅e principal⋅e :

Islam Eldifrawi

Visiteur de recherche indépendant - University of Sherbrooke

Aarash Feizi

Doctorat - McGill

Co-superviseur⋅e :

Adriana Romero Soriano

Collaborateur·rice de recherche - McGill

Nazia Hossain

Collaborateur·rice de recherche - McGill

Shenyang Huang

Collaborateur·rice alumni - McGill

Co-superviseur⋅e :

Anne Imouza

Doctorat - McGill

Superviseur⋅e principal⋅e :

Hugh Kelly

Collaborateur·rice de recherche - McGill University

Emma Kondrup

Doctorat - McGill

Co-superviseur⋅e :

Andrew Lin

Stagiaire de recherche - McGill University

Doctorat - McGill

Sitao Luan

Postdoctorat - McGill

Superviseur⋅e principal⋅e :

Shahrad Mohammadzadeh

Maîtrise recherche - McGill

Co-superviseur⋅e :

Visiteur de recherche indépendant - McGill

Collaborateur·rice alumni - McGill

Farimah Poursafaei

Collaborateur·rice alumni - McGill

Maîtrise recherche - McGill University

Dorsaf Sallami

Collaborateur·rice de recherche - McGill

Maîtrise recherche - McGill

Collaborateur·rice de recherche - McGill University

Jacob-Junqi Tian

Maîtrise recherche - McGill

Collaborateur·rice de recherche - UdeM

Superviseur⋅e principal⋅e :

Tong Wu

Collaborateur·rice de recherche - McGill University

Co-superviseur⋅e :

Doctorat - McGill

McGill University

Sveta Zhuk

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Démasquer les deepfakes grâce à l'IA

Billets de blogue

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.

16 décembre 2025

par

Victor Livernoche

Reihaneh Rabbany

Lire l'article

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

3 août 2021

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

par

Shenyang Huang

Reihaneh Rabbany

Lire l'article

Publications

EASE Configuration Facilitates A Reproducible Science of LLM Social Simulations

Maximilian Puelma Touzel

LLMs are increasingly deployed to simulate social interactions, yet many of the existing simulators remain ad hoc and monolithic. This lack … (voir plus)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 (prépublication)

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 (publié)

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… (voir plus)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 (prépublication)

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

Ta Thanh Thuy

Jiaqi Zhu

Xuan Liu

Lin Shang

Lihui Chen

Zheng Yilun

Sitao Luan

Understanding how people argue across ideological divides online is important for studying political polarization, misinformation, and conte… (voir plus)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.

2026-04-30

arXiv (prépublication)

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

Maximilian Puelma Touzel

Studies attempting to simulate human behavior with …

2026-04-29

arXiv (prépublication)

What do people want to fact-check?

Bijean Ghafouri

Dorsaf Sallami

Luca Luceri

Taylor Lynn Curtis

Emilio Ferrara

2026-02-10

arXiv (prépublication)

AI Epistemic Risks: Emerging Mechanisms &amp; Evidence

Mick Yang

Stephen Casper

Jonathan Stray

Jasmine Li

Cameron Jones

Anna Gausen

Natasha Jacques

Brian Christian

Bálint Gyevnár

Hannah Rose Kirk

ZHONGHAO HE

Dan Zhao (285025)

Siao Si Looi

J. Levy

Kobi Hackenburg

Elizabeth Seger

Matt Kowal

Michelle Malonza

Luke Hewitt

Hause Lin … (voir 10 de plus)

Maarten Sap

Dylan Hadfield-Menell

Thomas Costello

David Rand

Atoosa Kasirzadeh

Gordon Pennycook

Yoshua Bengio

Kellin Pelrine

2025-12-31

SSRN Electronic Journal (accepté)

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… (voir plus)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. These results demonstrate the critical role of high-quality, expert-driven datasets in advancing general-purpose computer-use agents.

2025-12-31

International Conference on Learning Representations (Accept (Poster))

openreview.net

Position: Time to Close The Validation Gap in LLM Social Simulations

Maximilian Puelma Touzel

LLM-based social simulations—in which many language model agents interact over multiple turns—are rapidly proliferating across policy an… (voir plus)alysis, epidemiology, and computational social science. Yet the field lacks consensus on how to validate these simulations, with evaluation methods that are sparse, inconsistent, and rarely shared across disciplinary silos. We argue this creates a serious risk: premature deployment of unvalidated simulators in high-stakes domains. Our position is that the field must pivot from expansion to consolidation, prioritizing methodological standardization—shared benchmarks, open data, and reproducible evaluation protocols grounded in social science and complex systems research. We outline a concrete research program organized around specific learning problems/benchmarks, providing a path toward answering the fundamental question: when are LLM social simulations useful modelling objects?

2025-12-31

International Conference on Machine Learning (Accept (regular))

openreview.net

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… (voir plus)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.

2025-12-14

arXiv (prépublication)

Large Language Model Applications in the Algebra Domain: A Systematic Review

Yajie Song

Yimei Zhang

Doina Precup

Maria Cutumisu

2025-12-05

Technology, Knowledge and Learning (publié)

Higher Order Transformers: Efficient Attention Mechanism for Tensor Structured Data

Soroush Omranpour

Transformers are now ubiquitous for sequence modeling tasks, but their extension to multi-dimensional data remains a challenge due to the qu… (voir plus)adratic cost of the attention mechanism. In this paper, we propose Higher-Order Transformers (HOT), a novel architecture designed to efficiently process data with more than two axes, i.e. higher-order tensors. To address the computational challenges associated with high-order tensor attention, we introduce a novel Kronecker factorized attention mechanism that reduces the attention cost to quadratic in each axis' dimension, rather than quadratic in the total size of the input tensor. To further enhance efficiency, HOT leverages kernelized attention, reducing the complexity to linear. This strategy maintains the model's expressiveness while enabling scalable attention computation. We validate the effectiveness of HOT on two high-dimensional tasks, including multivariate time series forecasting, and 3D medical image classification. Experimental results demonstrate that HOT achieves competitive performance while significantly improving computational efficiency, showcasing its potential for tackling a wide range of complex, multi-dimensional data.

2025-11-15

TMLR (accepté)