Reihaneh Rabbany

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

Aurélien Bück-Kaeffer

Master's Research - McGill University

Jacob Chmura

Master's Research - McGill University

Principal supervisor :

Research Intern - Université de Montréal

Principal supervisor :

Johnny Dellas

Research Intern - McGill University

Islam Eldifrawi

Independent visiting researcher - University of Sherbrooke

Aarash Feizi

PhD - McGill University

Co-supervisor :

Adriana Romero Soriano

Collaborating researcher - McGill University

Nazia Hossain

Research Intern - McGill University

Shenyang Huang

Collaborating Alumni - McGill University

Co-supervisor :

Anne Imouza

PhD - McGill University

Principal supervisor :

Hugh Kelly

Collaborating researcher - McGill University University

Emma Kondrup

PhD - McGill University

Co-supervisor :

Andrew Lin

Research Intern - McGill University University

Victor Livernoche

PhD - McGill University

Sitao Luan

Postdoctorate - McGill University

Principal supervisor :

Guillaume Rabusseau

Shahrad Mohammadzadeh

Master's Research - McGill University

Co-supervisor :

Independent visiting researcher - McGill University

Collaborating Alumni - McGill University

Farimah Poursafaei

Collaborating Alumni - McGill University

Master's Research - McGill University University

Dorsaf Sallami

Collaborating researcher - McGill University

Sneheel Sarangi

Master's Research - McGill University

Collaborating researcher - McGill University University

Jacob-Junqi Tian

Master's Research - McGill University

Collaborating researcher - Université de Montréal

Principal supervisor :

Guillaume Rabusseau

Li Wei Wang

Research Intern - McGill University

Tong Wu

Collaborating researcher - McGill University University

Co-supervisor :

Zachary Yang

PhD - McGill University

Shirley Zhao

Research Intern - McGill University University

Sveta Zhuk

Master's Research - Université de Montréal

Principal supervisor :

Unmasking deepfakes with AI

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

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

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)

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

2025-06-08

CODEML @ International Conference on Machine Learning (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)

From Intuition to Understanding: Using AI Peers to Overcome Physics Misconceptions

Ruben Weijers

Denton Wu

Hannah Betts

Tamara Jacod

Yuxiang Guan

Vidya Sujaya

Kushal Dev

Toshali Goel

William Delooze

Ying Wu

Generative AI has the potential to transform personalization and accessibility of education. However, it raises serious concerns about accur… (see more)acy and helping students become independent critical thinkers. In this study, we designed a helpful yet fallible AI "Peer" to help students correct fundamental physics misconceptions related to Newtonian mechanic concepts. In contrast to approaches that seek near-perfect accuracy to create an authoritative AI tutor or teacher, we directly inform students that this AI can answer up to 40\% of questions incorrectly. In a randomized controlled trial with 165 students, those who engaged in targeted dialogue with the AI Peer achieved post-test scores that were, on average, 10.5 percentage points higher—with over 20 percentage points higher normalized gain—than a control group that discussed physics history. Qualitative feedback indicated that 91% of the treatment group's AI interactions were rated as helpful. Furthermore, by comparing student performance on pre- and post-test questions about the same concept, along with experts' annotations of the AI interactions, we find initial evidence suggesting the improvement in performance does not depend on the correctness of the AI. With further research, the AI Peer paradigm described here could open new possibilities for how we learn, adapt to, and grow with AI.

2025-03-04

ICLR.cc/2025/Workshop/Bi-Align (poster)

Rethinking Anti-Misinformation AI

This paper takes a position on how anti-misinformation AI works should be developed for the online misinformation context. We observe that t… (see more)he current literature is dominated by works that produce more information for users to process and that this function faces various challenges in bringing meaningful effects to reality. We use anti-misinformation insights from other domains to suggest a redirection of the existing line of work and identify an under-explored opportunity AI can facilitate exploring.

2025-03-04

ICLR.cc/2025/Workshop/HAIC (published)

Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy

Khaoula Chehbouni

Martine De Cock

Gilles Caporossi

Afaf Taïk

Golnoosh Farnadi

The increased screen time and isolation caused by the COVID-19 pandemic have led to a significant surge in cases of online grooming, which i… (see more)s the use of strategies by predators to lure children into sexual exploitation. Previous efforts to detect grooming in industry and academia have involved accessing and monitoring private conversations through centrally-trained models or sending private conversations to a global server. In this work, we implement a privacy-preserving pipeline for the early detection of sexual predators. We leverage federated learning and differential privacy in order to create safer online spaces for children while respecting their privacy. We investigate various privacy-preserving implementations and discuss their benefits and shortcomings. Our extensive evaluation using real-world data proves that privacy and utility can coexist with only a slight reduction in utility.

2025-01-20

arXiv (preprint)

RL Fine-Tuning Heals OOD Forgetting in SFT

Sicheng Lyu

Mohammad Hamdaqa

The two-stage fine-tuning paradigm of Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has empirically shown better reas… (see more)oning performance than one-stage SFT for the post-training of Large Language Models (LLMs). However, the evolution and mechanism behind the synergy of SFT and RL are still under-explored and inconclusive. In our study, we find the well-known claim "SFT memorizes, RL generalizes" is over-simplified, and discover that: (1) OOD performance peaks at the early stage of SFT and then declines (OOD forgetting), the best SFT checkpoint cannot be captured by training/test loss; (2) the subsequent RL stage does not generate fundamentally better OOD capability, instead it plays an \textbf{OOD restoration} role, recovering the lost reasoning ability during SFT; (3) The recovery ability has boundaries, \ie{} \textbf{if SFT trains for too short or too long, RL cannot recover the lost OOD ability;} (4) To uncover the underlying mechanisms behind the forgetting and restoration process, we employ SVD analysis on parameter matrices, manually edit them, and observe their impacts on model performance. Unlike the common belief that the shift of model capacity mainly results from the changes of singular values, we find that they are actually quite stable throughout fine-tuning. Instead, the OOD behavior strongly correlates with the \textbf{rotation of singular vectors}. Our findings re-identify the roles of SFT and RL in the two-stage fine-tuning and discover the rotation of singular vectors as the key mechanism. %reversing the rotations induced by SFT, which shows recovery from forgetting, whereas imposing the SFT parameter directions onto a RL-tuned model results in performance degradation. Code is available at https://github.com/xiaodanguoguo/RL_Heals_SFT

2024-12-31

arXiv (preprint)

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/.

2024-12-31

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

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

Shahrad Mohammadzadeh

Juan David Guerra

Marco Bonizzato

Golnoosh Farnadi

As large language models (LLMs) become increasingly prevalent, concerns about their reliability, particularly due to hallucinations - factua… (see more)lly inaccurate or irrelevant outputs - have grown. Our research investigates the relationship between the uncertainty in training dynamics and the emergence of hallucinations. Using models from the Pythia suite and several hallucination detection metrics, we analyze hallucination trends and identify significant variance during training. To address this, we propose Sensitivity Dropout (SenD), a novel training protocol designed to reduce hallucination variance during training by deterministically dropping embedding indices with significant variability. In addition, we develop an unsupervised hallucination detection metric, Efficient EigenScore (EES), which approximates the traditional EigenScore in 2x speed. This metric is integrated into our training protocol, allowing SenD to be both computationally scalable and effective at reducing hallucination variance. SenD improves test-time reliability of Pythia and Meta's Llama models by up to 17% and enhances factual accuracy in Wikipedia, Medical, Legal, and Coding domains without affecting downstream task performance.

2024-12-31

Association for Computational Linguistics (published)

Online Influence Campaigns: Strategies and Vulnerabilities

Andreea Musulan

Veronica Xia

Ethan Kosak-Hine

Tom Gibbs

Vidya Sujaya

U. Montr'eal

Ivado

M. University

In order to combat the creation and spread of harmful content online, this paper defines and contextualizes the concept of inauthentic, soci… (see more)etal-scale manipulation by malicious actors. We review the literature on societally harmful content and how it proliferates to analyze the manipulation strategies used by such actors and the vulnerabilities they target. We also provide an overview of three case studies of extensive manipulation campaigns to emphasize the severity of the problem. We then address the role that Artificial Intelligence plays in the development and dissemination of harmful content, and how its evolution presents new threats to societal cohesion for countries across the globe. Our survey aims to increase our understanding of not just particular aspects of these threats, but also the strategies underlying their deployment, so we can effectively prepare for the evolving cybersecurity landscape.

2024-12-31

arXiv (preprint)