Portrait of Reihaneh Rabbany

Reihaneh Rabbany

Core Academic Member
Canada CIFAR AI Chair
Assistant Professor, McGill University, School of Computer Science
Research Topics
Data Mining
Graph Neural Networks
Learning on Graphs
Natural Language Processing
Representation Learning

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

Collaborating researcher - Concordia University
Master's Research - McGill University
Master's Research - McGill University
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PhD - McGill University
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Collaborating Alumni - McGill University
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Research Intern - McGill University
Master's Research - McGill University
PhD - McGill University
Postdoctorate - McGill University
Principal supervisor :
Master's Research - McGill University
Co-supervisor :
Collaborating researcher - McGill University
Master's Research - McGill University
Collaborating Alumni - McGill University
Co-supervisor :
Collaborating Alumni - McGill University
Collaborating researcher
Collaborating researcher - McGill University University
Collaborating researcher - McGill University
Research Intern - McGill University
Master's Research - McGill University
Master's Research - Université de Montréal
Principal supervisor :
Collaborating researcher - McGill University
Collaborating researcher - Université de Montréal
Principal supervisor :
PhD - McGill University
Research Intern - McGill University
Master's Research - Université de Montréal
Principal supervisor :

Publications

A Guide to Misinformation Detection Data and Evaluation
Gabrielle Péloquin-Skulski
James Zhou
Florence Laflamme
Luke Yuxiang Guan
A Guide to Misinformation Detection Data and Evaluation
Gabrielle Péloquin-Skulski
James Zhou
Florence Laflamme
Yuxiang Guan
Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this probl… (see more)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].
Responsible AI Day
Ebrahim Bagheri
Faezeh Ensan
Calvin Hillis
Robin Cohen
Sébastien Gambs
Responsible AI Day
Ebrahim Bagheri
Faezeh Ensan
Calvin Hillis
Robin Cohen
Sébastien Gambs
Temporal Graph Learning Workshop
Daniele Zambon
Andrea Cini
Julia Gastinger
Micheal Bronstein
Temporal Graph Learning Workshop
Daniele Zambon
Andrea Cini
Julia Gastinger
Michael Bronstein
Temporal Graph Learning Workshop
Daniele Zambon
Andrea Cini
Julia Gastinger
Micheal Bronstein
Hallucination Detox: Sensitivity Dropout (SenD) for Large Language Model Training
Veracity: An Open-Source AI Fact-Checking System
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.
Veracity: An Open-Source AI Fact-Checking System
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.
A Systematic Literature Review of Large Language Model Applications in the Algebra Domain
AIF-GEN: Open-Source Platform and Synthetic Dataset Suite for Reinforcement Learning on Large Language Models