Publications

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations
Connor Stone
Alexandre Adam
Adam Coogan
M. J. Yantovski-Barth
Andreas Filipp
Landung Setiawan
Cordero Core
Ronan Legin
Charles Wilson
Gabriel Missael Barco
Yashar Hezaveh
Laurence Perreault-Levasseur
2025-12-31
Zenodo (published)
doi.org
arxiv.org
Charge allostatique et dimensions transdiagnostiques des patients admis à l’urgence psychiatrique : un protocole de projet utilisant la Biobanque Signature
Robert‐Paul Juster
Cécile Le Page
Névéna Chuntova
Enzo Cipriani
Clara Morin
Claudia Trudel-Fitzgerald
Marie‐France Marin
Ahmed Jérôme Romain
Steve Geoffrion
Stéphane Guay
Vincent Taschereau‐Dumouchel
Stephane Potvin
Naguib Mcchawar
Caroline Ménard
Eugénie Samson-Daoust
Charles‐Édouard Giguère
Janick Boissonneault
Helen Findlay
Jasmine Boulette
Michelle Lonergan … (see 5 more)
Bahram Armoon
Alain Lesage
Alexandre Hudon
Christophe Longpré-Poirier
Emmanuel Stip
Objectif Les personnes en situation de crise aiguë qui sollicitent les services d’urgence psychiatrique manifestent une détresse psychol… (see more)ogique sévère et présentent probablement des signes physiologiques de stress chronique. Toutefois, comment mesurer au mieux le stress chronique ? Notre étude vise à évaluer la charge allostatique (CA), c’est-à-dire l’ « usure » des systèmes biologiques que nous pouvons mesurer à l’aide de différents marqueurs biologiques appelés biomarqueurs. Méthode Dans ce protocole utilisant les données de la Biobanque Signature, nous souhaitons évaluer les dimensions biologiques et psychosociales liées aux changements des symptômes psychiatriques chez les patient(e)s consultant à l’urgence psychiatrique. L’évaluation sera faite à 4 moments consécutifs : (T1) 1-4 jours après leur admission à l’urgence ; (T2) à la fin de leur hospitalisation (en moyenne 2 semaines plus tard) ; (T3) lors d’un suivi en consultation externe (en moyenne 2 mois plus tard) ; et (T4) environ 1 an plus tard. Nous prévoyons que : 1) la CA à T1 permettra de distinguer différents troubles psychiatriques ; 2) une CA plus élevée permettra de prédire l’évolution des symptômes au fil du temps ; et 3) que la combinaison de facteurs de risque et de protection modifiables (p. ex. les comportements liés à la santé, les expériences psychosociales) renforcera ou atténuera ces associations observées. Nous avons obtenu un financement conséquent des IRSC afin d’analyser les échantillons de cheveux, de salive et de sang, prélevés et conservés par la Biobanque Signature. Cette analyse constituera des mesures à T1 ( N = 1984 ; 40 % de femmes ; âge moyen de 41 ans, avec une fourchette allant de 18 à 81 ans) ainsi qu’à T2 ( n = 685), T3 ( n = 606) et T4 ( n = 370). Résultats Le projet proposé permettra d’indexer la CA chez les patient(e)s et les 149 participant(e)s témoins sans trouble psychiatrique récent. Parmi les sous-échantillons avec des mesures T2, T3 et T4, nous évaluerons simultanément, de manière prospective, les changements de la CA et les trajectoires des symptômes psychiatriques. Conclusion De l’admission à la rémission, l’étude des patient(e)s en urgence psychiatrique est une occasion unique d’évaluer des profils extrêmes qui peuvent contribuer à des approches de médecine de précision. La Signature Biobanque apportera des données uniques sur les signatures biologiques des troubles psychiatriques et leur évolution dans le temps, tout en offrant des possibilités de connaissances uniques et renforcement des capacités pour les années à venir, avec le soutien d’une équipe transdisciplinaire.
2025-12-31
Santé Mentale au Québec (published)
doi.org
Clinicians' ethical considerations on use of AI-enabled technologies for primary prevention of cardiovascular disease in female patients
Ting Wang
Amrita Sandhu
Kyle Vamvakas
Howard Bergman
Roland Grad
Isabelle Vedel
Marie-Pierre Gagnon
Shahram Yousefi
Samira Abbasgholizadeh-Rahimi
Artificial intelligence (AI) is increasingly used in healthcare to support the prevention and management of cardiovascular disease (CVD); ho… (see more)wever, its ethical implications in clinical practice, particularly for female patients, remain insufficiently explored. This study aimed to explore clinicians' perspectives on the ethical use of AI for preventing and managing cardiovascular disease (CVD) in female patients. A qualitative descriptive design was employed using semi-structured interviews with clinicians practicing in Montreal, Canada. Interviews were conducted online, audio-recorded with participants’ consent, and transcribed for analysis. Data were analyzed using deductive thematic analysis informed by ethical domains in the established AI frameworks. Ethical approval was obtained from McGill University’s Research Ethics Board. The study adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines. A final sample of twelve clinicians was interviewed, with each interview lasting approximately 60 minutes. Four key themes emerged: fairness, privacy and security, explainability, and data integrity. Clinicians expressed concerns that AI-enabled technologies may introduce or reinforce biases affecting certain populations, including older adults, individuals with limited digital literacy, and those lacking reliable internet access or access to digital technologies. Participants also raised concerns regarding data integrity, privacy and security, and emphasized the importance of transparent and understandable AI outputs to support clinical decision-making. Ethical considerations are fundamental to the responsible integration of AI in cardiovascular care. Addressing concerns related to fairness, privacy and security, explainability, and data integrity may strengthen clinician trust and support the implementation of AI-enabled technologies in clinical practice. Future research should explore practical approaches to address these concerns and assess how ethically informed AI systems can be implemented effectively in clinical practice.
2025-12-31
FACETS (published)
doi.org
Clinicians' needs and perspectives on use of AI-enabled technologies for primary prevention of cardiovascular disease in female patients
Ting Wang
Amrita Sandhu
Kyle Vamvakas
Howard Bergman
Roland Grad
Isabelle Vedel
Marie-Pierre Gagnon
Shahram Yousefi
Samira Abbasgholizadeh-Rahimi
This study aimed to (1) explore clinicians’ perspectives of cardiovascular disease (CVD) and risk management in female patients and (2) de… (see more)scribe clinicians’ needs and desired features in AI-enabled tools for primary prevention and management of CVD among female patients. This work employed a qualitative description design. We conducted semi-structured interviews with 12 clinicians in Montreal, Canada. We used inductive thematic analysis to interpret the data. Seven themes emerged from the analysis. Three themes were related to the first objective: complexity in clinical decision-making, limitations of CVD risk assessment tools, and resources and health literacy. Four themes were related to the second objective: AI efficiency, multilingual design, electronic medical record integration, and ease of use. Clinicians reported challenges in supporting female patients at higher risk for CVD and expressed concerns about existing decision support tools. They showed openness to AI-enabled tools like Xi-Care and provided input on desired features to ensure their usability and effectiveness. There is a demand to support clinicians in the primary prevention and management of CVD among female patients. AI-enabled tools could effectively address this demand, provided their development prioritizes clinicians' needs and perspectives to ensure safe and effective implementation.
2025-12-31
FACETS (published)
doi.org
A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustness
Leo Schwinn
Moritz Ladenburger
Tim Beyer
Mehrnaz Mofakhami
Gauthier Gidel
Stephan Günnemann
Automated \enquote{LLM-as-a-Judge} frameworks have become the de facto standard for scalable evaluation across natural language processing. … (see more)For instance, in safety evaluation, these judges are relied upon to evaluate harmfulness in order to benchmark the robustness of safety against adversarial attacks. However, we show that existing validation protocols fail to account for substantial distribution shifts inherent to red-teaming: diverse victim models exhibit distinct generation styles, attacks distort output patterns, and semantic ambiguity varies significantly across jailbreak scenarios. Through a comprehensive audit using 6642 human-verified labels, we reveal that the unpredictable interaction of these shifts often causes judge performance to degrade to near random chance. This stands in stark contrast to the high human agreement reported in prior work. Crucially, we find that many attacks inflate their success rates by exploiting judge insufficiencies rather than eliciting genuinely harmful content. To enable more reliable evaluation, we propose ReliableBench, a benchmark of behaviors that remain more consistently judgeable, and JudgeStressTest, a dataset designed to expose judge failures. (Data in supplement).
2025-12-31
International Conference on Machine Learning (Accept (regular))
doi.org
openreview.net
$\mu$LO: Compute-Efficient Meta-Generalization of Learned Optimizers
Benjamin Therien
Charles-Etienne Joseph
Boris Knyazev
Edouard Oyallon
Irina Rish
Eugene Belilovsky
Learned optimizers (LOs) have the potential to significantly reduce the wall-clock training time of neural networks. However, they can strug… (see more)gle to optimize unseen tasks (*meta-generalize*), especially when training networks wider than those seen during meta-training. To address this, we derive the Maximal Update Parametrization (
2025-12-31
International Conference on Learning Representations (Accept (Poster))
openreview.net
openreview.net
Contractive Diffusion Policies: Robust Action Diffusion via Contractive Score-Based Sampling with Differential Equations
Amin Abyaneh
Charlotte Morissette
Mohamad H. Danesh
Anas El Houssaini
David Meger
Gregory Dudek
Hsiu-Chin Lin
Diffusion policies have emerged as powerful generative models for offline policy learning, whose sampling process can be rigorously characte… (see more)rized by a score function guiding a Stochastic Differential Equation (SDE). However, the same score-based SDE modeling that grants diffusion policies the flexibility to learn diverse behavior also incurs solver and score-matching errors, large data requirements, and inconsistencies in action generation. While less critical in image generation, these inaccuracies compound and lead to failure in continuous control settings. We introduce Contractive Diffusion Policies (CDPs) to induce contractive behavior in the diffusion sampling dynamics. Contraction pulls nearby flows closer to enhance robustness against solver and score-matching errors while reducing unwanted action variance. We develop an in-depth theoretical analysis along with a practical implementation recipe to incorporate CDPs into existing diffusion policy architectures with minimal modification and computational cost. We evaluate CDPs for offline learning by conducting extensive experiments in simulation and real-world settings. Across benchmarks, CDPs often outperform baseline policies, with pronounced benefits under data scarcity.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net
ControBench: An Interaction-Aware Benchmark for Controversial Discourse Analysis on Social Networks
Ta Thanh Thuy
Jiaqi Zhu
Xuan Liu
Lin Shang
Reihaneh Rabbany
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)
doi.org
arxiv.org
Decoding Dynamic Visual Experience from Calcium Imaging via Cell-Pattern-Aware SSL
Sangyoon Bae
Mehdi Azabou
Blake A Richards
Jiook Cha
Self-supervised learning (SSL) holds a great deal of promise for applications in neuroscience, due to the lack of large-scale, consistently … (see more)labeled neural datasets. However, most neural datasets contain heterogeneous populations that mix stable, predictable cells with highly stochastic, stimulus-contingent ones, which has made it hard to identify consistent activity patterns during SSL. As a result, self-supervised pretraining has yet to show clear signs of benefits from scale on neural data. Here, we present a novel approach to self-supervised pretraining, POYO-SSL that exploits the heterogeneity of neural data to improve pretraining and achieve benefits of scale. Specifically, in POYO-SSL we pretrain only on predictable neurons---identified on the pretraining split via simple higher-order statistics (skewness and kurtosis)---then we fine-tune on the unpredictable population for downstream tasks. On the Allen Brain Observatory dataset, this strategy yields approximately 12--13\% relative gains over from-scratch training and exhibits smooth, monotonic scaling with model size. In contrast, existing state-of-the-art baselines plateau or destabilize as model size increases. By making predictability an explicit metric for crafting the data diet, POYO-SSL turns heterogeneity from a liability into an asset, providing a robust, biologically grounded recipe for scalable neural decoding and a path toward foundation models of neural dynamics.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
openreview.net
openreview.net
A Deep Learning and Inertia-Aware Load Shedding Framework for Mitigating Load-Altering Attacks
Anoosh Dini
Keyhan Sheshyekani
Hanane Dagdougui
The widespread integration of information and communication technologies into modern power systems has increased their vulnerability to cybe… (see more)r-physical threats, such as load-altering attacks (LAA). These attacks can cause rapid load changes, potentially triggering protective mechanisms like under-frequency load shedding (UFLS). Existing approaches for mitigating these attacks are limited, and they mostly rely on preventive measures or neglect system dynamics. In this paper, we propose a novel online framework for the detection and mitigation of LAAs that addresses these limitations. The detection component employs a convolutional neural network–long short-term memory autoencoder (CNN-LSTM AE) architecture to capture anomalies in load consumption data. For mitigation, we propose an inertia-aware load shedding scheme that dynamically adjusts the shedding amount based on the real-time frequency and the magnitude of the attack. This approach prevents overshedding caused by predefined UFLS relay settings and mitigates undershedding by considering the system’s real-time inertia. To this end, a variable forgetting factor recursive least squares (VFF-RLS) algorithm is proposed, which can track inertia variations within a few seconds. The proposed framework is compatible with both synchronous generator-based and converter-interfaced generator-dominated grids. Simulations indicate the effectiveness of the proposed framework in maintaining frequency stability under a wide range of attack scenarios.
2025-12-31
IEEE Access (published)
doi.org
Deep neural networks divide and conquer dihedral multiplication
Sihui Wei
Gavin McCracken
Gabriela Moisescu-Pareja
Harley Wiltzer
Doina Precup
Irina Rish
Jonathan Love
We find multilayer perceptrons and transformers both universally learn an instantiation of the same divide-and-conquer algorithm that requir… (see more)es only a logarithmic number of neural representations to solve dihedral multiplication. Clustering neurons based on similar activation behaviour reveals remarkably clear structure: each neural representation corresponds to a Cayley graph. To our knowledge, this is the first work that fully characterizes and describes all neural representations that are learnable on a dataset, while prior work on group multiplications studied neuron-level behavior, or preliminarily investigated cluster behavior. Thus, we can understand the algorithm networks universally learn at three levels of abstraction: 1) Neurons activate on coset or approximate coset structure of the dihedral group. 2) Groups of neurons together form neural representations that act to divide the dataset into different subproblems, being Cayley graphs, where the equivalence class of the answer is computed. 3) The global algorithm then linearly combines each neural representation (subproblem) together at the logits. This work provides a deep case study and provides the community with a very well understood toy model for interpretability, as well as makes steps toward proving the conjecture that DNNs will divide and conquer all group multiplication tasks.
2025-12-31
International Conference on Machine Learning (Accept (regular))
openreview.net
openreview.net
Development and Deployment of Jami: Experiences with a Mobile Distributed Communication Platform
Yili Jin
Tianyi Yang
Adrien Béraud
Tao Yang
Sébastien Blin
Cyrille Béraud
Qiao Xiang
Xue Liu
2025-12-31
IEEE Transactions on Mobile Computing (published)
doi.org