Irina Rish

rishika.bhagwatkar@mila.quebec

Biographie

Irina Rish est professeure titulaire à l'Université de Montréal (UdeM), où elle dirige le Laboratoire d'IA autonome. Membre du corps professoral de Mila – Institut québécois d’intelligence artificielle, elle est titulaire d'une chaire d'excellence en recherche du Canada (CERC) et d'une chaire en IA Canada-CIFAR. Irina dirige le projet INCITE du ministère américain de l'Environnement au sujet des modèles de fondation évolutifs sur les superordinateurs Summit et Frontier à l'Oak Ridge Leadership Computing Facility (OLCF). Elle est cofondatrice et directrice scientifique de Nolano.ai.

Ses recherches actuelles portent sur les lois de mise à l'échelle neuronale et les comportements émergents (capacités et alignement) dans les modèles de fondation, ainsi que sur l'apprentissage continu, la généralisation hors distribution et la robustesse. Avant de se joindre à l'UdeM en 2019, Irina était chercheuse au Centre de recherche IBM Thomas J. Watson, où elle a travaillé sur divers projets à l'intersection des neurosciences et de l'IA, et dirigé le défi NeuroAI. Elle a reçu plusieurs prix IBM : ceux de l’excellence et de l’innovation exceptionnelle (2018), celui de la réalisation technique exceptionnelle (2017), et celui de l’accomplissement en recherche (2009). Elle détient 64 brevets et a écrit plus de 120 articles de recherche, plusieurs chapitres de livres, trois livres publiés et une monographie sur la modélisation éparse.

Étudiants actuels

Ivan Anokhin

Doctorat - UdeM

Co-superviseur⋅e :

Samira Ebrahimi Kahou

ivan.anokhin@mila.quebec

Doctorat - UdeM

Arjun Ashok

Doctorat - UdeM

Co-superviseur⋅e :

Alexandre Drouin

arjun.ashok@mila.quebec

Maîtrise recherche - UdeM

Doctorat - UdeM

ethan.caballero@mila.quebec

Pietro Cagnasso

Doctorat - Concordia

Superviseur⋅e principal⋅e :

pietro.cagnasso@mila.quebec

Mohammad Javad Darvishi Bayazi

Amin Darabi

Doctorat - UdeM

amin.darabi@mila.quebec

Collaborateur·rice de recherche - UdeM

mj.darvishi92@gmail.com

nadhir.hassen@mila.quebec

Wagner Drew

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

Mirco Ravanelli

drew.wagner@mila.quebec

Doctorat - UdeM

Visiteur de recherche indépendant - -

parviz.haggi@gmail.com

Nadhir Hassen

Collaborateur·rice de recherche - UdeM

Niklas Herbster

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Gauthier Gidel

niklas.herbster@gmail.com

prateek.humane@mila.quebec

Mai-Thi Ho

Maîtrise recherche - UdeM

mai-thi.ho@mila.quebec

Prateek Humane

Doctorat

arnav-kumar.jain@mila.quebec

Nizar Islah

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Eilif B. Muller

nizar.islah@mila.quebec

Doctorat - UdeM

Doctorat - UdeM

ayush.kaushal@mila.quebec

Arian Khorasani

Collaborateur·rice de recherche - UdeM

arian.khorasani@mila.quebec

Neeraj Kumar

Collaborateur·rice alumni - UdeM

neeraj.kumar@mila.quebec

Gwen Legate

Doctorat - Concordia

Superviseur⋅e principal⋅e :

gwendolyne.legate@mila.quebec

David Lemay

Maîtrise recherche - UdeM

david.lemay@mila.quebec

amin.mansouri@mila.quebec

Amin Mansouri

Collaborateur·rice alumni - UdeM

Collaborateur·rice de recherche

Doctorat - UdeM

Collaborateur·rice de recherche - UdeM

diganta.misra@mila.quebec

motahareh.pourrahimi@mila.quebec

Gabriela Moisescu-Pareja

Collaborateur·rice de recherche - McGill

Superviseur⋅e principal⋅e :

Doina Precup

moisescg@mila.quebec

Timothy Nest

Doctorat - UdeM

Co-superviseur⋅e :

Eilif B. Muller

timothy.nest@mila.quebec

Jonathan Pilault

Collaborateur·rice de recherche - Polytechnique

Doctorat - McGill

Superviseur⋅e principal⋅e :

Pouya Bashivan

Mahta Ramezanian

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Guillaume Dumas

mahta.ramezanian@mila.quebec

Matthew Riemer

Doctorat - UdeM

matthew.riemer@mila.quebec

Alexis Roger

Doctorat - McGill

Superviseur⋅e principal⋅e :

Blake Richards

alexis.roger@mila.quebec

munish.sathish-kumar@mila.quebec

Munish Sathish Kumar

Collaborateur·rice de recherche

Zibo Shang

Doctorat - UdeM

zibo.shang@mila.quebec

Vaibhav Singh

Doctorat - Concordia

Superviseur⋅e principal⋅e :

vaibhav.singh@mila.quebec

Gopeshh Subbaraj

Doctorat - UdeM

gopeshh.subbaraj@mila.quebec

Doctorat - UdeM

Co-superviseur⋅e :

benjamin.therien@mila.quebec

Collaborateur·rice de recherche - UdeM

tejas.vaidhya@mila.quebec

andrew.williams@mila.quebec

Sihui Wei

Baccalauréat - McGill

sihui.wei@mila.quebec

Andrew Williams

Doctorat - UdeM

Co-superviseur⋅e :

Nicolas Chapados

daria.yasafova@mila.quebec

Daria Yasafova

Maîtrise recherche - UdeM

He Zhu

Doctorat - McGill

he.zhu@mila.quebec

Publications

Critical Role of EEG Signals in Assessment of Sex-Specific Insights in Neurological Diagnostics via Machine Learning Approach

Mohammad-Javad Darvishi-Bayazi

Mohammad Sajjad Ghaemi

Jocelyn Faubert

Abstract

Early detection and diagnosis of pathology are essential for efficient treatment and therapeutic … (voir plus)interventions. The emergence of Artificial Intelligence (AI) and deep machine learning techniques have demonstrated the promising capability of brain imaging data to predict various pathological diseases. However, plenty of diseases have imbalanced distribution across different sexes. Furthermore, the impact of sex-specific patterns and biomarkers in predicting diseases has remained unexplored as a fundamental subject matter to inform the treatment paradigms. This paper underscored the generalization and transferability of sex-related patterns in functional data, specifically Electroencephalogram (EEG) signals through Artificial Deep Neural Networks. We conducted training on a broad spectrum of EEG recordings involving participants ranging from 221 to 12,000, including healthy and pathological subjects. Our evaluation leveraged datasets from various sources and participant groups, featuring distribution shifts. While the artificial models demonstrated accurate sex detection on datasets without fine-tuning, their performance declined with significant distribution shifts. Furthermore, we explored the relationship between sex and pathology by visualizing salient features for target detection in distinct subgroups. Our findings revealed unprecedented insights into the negligible role of sex-specific patterns in pathology detection despite the presence of prominent and consistent patterns within sex groups. These results are essential for developing more robust and unbiased AI models for disease prediction and informing the treatment paradigms.

2025-12-09

Scientific Reports (publié)

CAVE: Detecting and Explaining Commonsense Anomalies in Visual Environments

Rishika Bhagwatkar

Syrielle Montariol

Angelika Romanou

Beatriz Borges

Antoine Bosselut

2025-10-31

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (publié)

Indirect Prompt Injections: Are Firewalls All You Need, or Stronger Benchmarks?

Kevin Kasa

Graham W. Taylor

Krishnamurthy Dj Dvijotham

Alexandre Lacoste

AI agents are vulnerable to indirect prompt injection attacks, where malicious instructions embedded in external content or tool outputs cau… (voir plus)se unintended or harmful behavior. Inspired by the well-established concept of firewalls, we show that a simple, modular and model-agnostic defense operating at the agent--tool interface achieves perfect security (0% or the lowest possible attack success rate) with high utility (task success rate) across four public benchmarks: AgentDojo, Agent Security Bench, InjecAgent and tau-Bench, while achieving a state-of-the-art security-utility tradeoff compared to prior results. Specifically, we employ a defense based on two firewalls: a Tool-Input Firewall (Minimizer) and a Tool-Output Firewall (Sanitizer). Unlike prior complex approaches, this firewall defense makes minimal assumptions on the agent and can be deployed out-of-the-box, while maintaining strong performance without compromising utility. However, our analysis also reveals critical limitations in these existing benchmarks, including flawed success metrics, implementation bugs, and most importantly, weak attacks, hindering significant progress in the field. To foster more meaningful progress, we present targeted fixes to these issues for AgentDojo and Agent Security Bench while proposing best-practices for more robust benchmark design. Further, we demonstrate that although these firewalls push the state-of-the-art on existing benchmarks, it is still possible to bypass them in practice, underscoring the need to incorporate stronger attacks in security benchmarks. Overall, our work shows that existing agentic security benchmarks are easily saturated by a simple approach and highlights the need for stronger agentic security benchmarks with carefully chosen evaluation metrics and strong adaptive attacks.

2025-10-05

ArXiv (prépublication)

A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy

Ola Ahmad

Deep learning models operating in the image domain are vulnerable to small input perturbations. For years, robustness to such perturbations … (voir plus)was pursued by training models from scratch (i.e., with random initializations) using specialized loss objectives. Recently, robust fine-tuning has emerged as a more efficient alternative: instead of training from scratch, pretrained models are adapted to maximize predictive performance and robustness. To conduct robust fine-tuning, practitioners design an optimization strategy that includes the model update protocol (e.g., full or partial) and the specialized loss objective. Additional design choices include the architecture type and size, and the pretrained representation. These design choices affect robust generalization, which is the model's ability to maintain performance when exposed to new and unseen perturbations at test time. Understanding how these design choices influence generalization remains an open question with significant practical implications. In response, we present an empirical study spanning 6 datasets, 40 pretrained architectures, 2 specialized losses, and 3 adaptation protocols, yielding 1,440 training configurations and 7,200 robustness measurements across five perturbation types. To our knowledge, this is the most diverse and comprehensive benchmark of robust fine-tuning to date. While attention-based architectures and robust pretrained representations are increasingly popular, we find that convolutional neural networks pretrained in a supervised manner on large datasets often perform best. Our analysis both confirms and challenges prior design assumptions, highlighting promising research directions and offering practical guidance.

2025-09-28

NeurIPS.cc/2025/Workshop/Reliable_ML (publié)

Continual Pre-training of MoEs: How robust is your router?

Benjamin Therien

Charles-Etienne Joseph

Zain Sarwar

Ashwinee Panda

Anirban Das

Shi-Xiong Zhang

Stephen Rawls

Sambit Sahu

2025-09-25

TMLR (accepté)

Beyond Naive Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs

Arjun Ashok

Andrew R. Williams

Vincent Zhihao Zheng

Nicolas Chapados

Étienne Marcotte

Valentina Zantedeschi

Alexandre Drouin

Forecasting in real-world settings requires models to integrate not only historical data but also relevant contextual information, often ava… (voir plus)ilable in textual form. While recent work has shown that large language models (LLMs) can be effective context-aided forecasters via naïve direct prompting, their full potential remains underexplored. We address this gap with 4 strategies, providing new insights into the zero-shot capabilities of LLMs in this setting. ReDP improves interpretability by eliciting explicit reasoning traces, allowing us to assess the model's reasoning over the context independently from its forecast accuracy. CorDP leverages LLMs solely to refine existing forecasts with context, enhancing their applicability in real-world forecasting pipelines. IC-DP proposes embedding historical examples of context-aided forecasting tasks in the prompt, substantially improving accuracy even for the largest models. Finally, RouteDP optimizes resource efficiency by using LLMs to estimate task difficulty, and routing the most challenging tasks to larger models. Evaluated on different kinds of context-aided forecasting tasks from the CiK benchmark, our strategies demonstrate distinct benefits over naïve prompting across LLMs of different sizes and families. These results open the door to further simple yet effective improvements in LLM-based context-aided forecasting.

2025-09-22

BERT2S @ Neural Information Processing Systems (poster)

Revisiting Replay and Gradient Alignment for Continual Pre-Training of Large Language Models

Matthew D Riemer

Tsuguchika Tabaru

Hiroaki Kingetsu

A. Chandar

2025-09-21

NeurIPS.cc/2025/Workshop/WiML (publié)

Persistent Instability in LLM's Personality Measurements: Effects of Scale, Reasoning, and Conversation History

Tommaso Tosato

Saskia Helbling

Yorguin-Jose Mantilla-Ramos

Mahmood Hegazy

Alberto Tosato

D. Lemay

Guillaume Dumas

2025-08-05

ArXiv (prépublication)

Beyond Cosine Decay: On the effectiveness of Infinite Learning Rate Schedule for Continual Pre-training

The ever-growing availability of unlabeled data presents both opportunities and challenges for training artificial intelligence systems. Whi… (voir plus)le self-supervised learning (SSL) has emerged as a powerful paradigm for extracting meaningful representations from vast amounts of unlabeled data, existing methods still struggle to adapt to the non-stationary, non-IID nature of real-world data streams without forgetting previously learned knowledge. Recent works have adopted a repeated cosine annealing schedule for large-scale continual pre-training; however, these schedules (1) inherently cause forgetting during the re-warming phase and (2) have not been systematically compared to existing continual SSL methods. In this work, we systematically compare the widely used cosine schedule with the recently proposed infinite learning rate schedule and empirically find the latter to be a more effective alternative. Our extensive empirical evaluation across diverse image and language datasets demonstrates that the infinite learning rate schedule consistently enhances continual pre-training performance compared to a repeated cosine decay without being restricted to a fixed iteration budget. For instance, in a small-scale MAE pre-training setup, it outperforms several strong baselines from the literature. We then scale up our experiments to larger MAE pre-training and autoregressive language model pre-training. Our results show that the infinite learning rate schedule remains effective at scale, surpassing repeated cosine decay for both MAE pre-training and zero-shot LM benchmarks.

2025-06-10

ICML.cc/2025/Workshop/ES-FoMo-III (publié)

MuLoCo: Muon is a practical inner optimizer for DiLoCo

Benjamin Therien

Xiaolong Huang

2025-06-10

ICML.cc/2025/Workshop/ES-FoMo-III (publié)

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field

Vanessa Hadid

Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive proces… (voir plus)ses with an unparalleled combination of high temporal and spatial precision. MEG data analytics has always relied on advanced signal processing and mathematical and statistical tools for various tasks ranging from data cleaning to probing the signals' rich dynamics and estimating the neural sources underlying the surface-level recordings. Like in most domains, the surge in Artificial Intelligence (AI) has led to the increased use of Machine Learning (ML) methods for MEG data classification. More recently, an emerging trend in this field is using Artificial Neural Networks (ANNs) to address many MEG-related tasks. This review provides a comprehensive overview of how ANNs are being used with MEG data from three vantage points: First, we review work that employs ANNs for MEG signal classification, i.e., for brain decoding. Second, we report on work that has used ANNs as putative models of information processing in the human brain. Finally, we examine studies that use ANNs as techniques to tackle methodological questions in MEG, including artifact correction and source estimation. Furthermore, we assess the current strengths and limitations of using ANNs with MEG and discuss future challenges and opportunities in this field. Finally, by establishing a detailed portrait of the field and providing practical recommendations for the future, this review seeks to provide a helpful reference for both seasoned MEG researchers and newcomers to the field who are interested in using ANNs to enhance the exploration of the complex dynamics of the human brain with MEG.

2025-05-26

Journal of Neural Engineering (publié)

Context is Key: A Benchmark for Forecasting with Essential Textual Information

Arjun Ashok

Andrew R. Williams

Étienne Marcotte

Valentina Zantedeschi

Jithendaraa Subramanian

Alexandre Lacoste

Forecasting is a critical task in decision-making across numerous domains. While historical numerical data provide a start, they fail to con… (voir plus)vey the complete context for reliable and accurate predictions. Human forecasters frequently rely on additional information, such as background knowledge and constraints, which can efficiently be communicated through natural language. However, in spite of recent progress with LLM-based forecasters, their ability to effectively integrate this textual information remains an open question. To address this, we introduce "Context is Key" (CiK), a time-series forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context, requiring models to integrate both modalities; crucially, every task in CiK requires understanding textual context to be solved successfully. We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters, and propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark. Our experiments highlight the importance of incorporating contextual information, demonstrate surprising performance when using LLM-based forecasting models, and also reveal some of their critical shortcomings. This benchmark aims to advance multimodal forecasting by promoting models that are both accurate and accessible to decision-makers with varied technical expertise. The benchmark can be visualized at https://servicenow.github.io/context-is-key-forecasting/v0/.

2025-04-30

International Conference on Machine Learning (poster)