Irina Rish

Biography

Irina Rish is a full professor at the Université de Montréal (UdeM), where she leads the Autonomous AI Lab, and a core academic member of Mila – Quebec Artificial Intelligence Institute.

In addition to holding a Canada Excellence Research Chair (CERC) and a CIFAR Chair, she leads the U.S. Department of Energy’s INCITE project on Scalable Foundation Models on Summit & Frontier supercomputers at the Oak Ridge Leadership Computing Facility. She co-founded and serves as CSO of Nolano.ai.

Rish’s current research interests include neural scaling laws and emergent behaviors (capabilities and alignment) in foundation models, as well as continual learning, out-of-distribution generalization and robustness.

Before joining UdeM in 2019, she was a research scientist at the IBM T.J. Watson Research Center, where she worked on various projects at the intersection of neuroscience and AI, and led the Neuro-AI challenge. She was awarded the IBM Eminence & Excellence Award and IBM Outstanding Innovation Award (2018), IBM Outstanding Technical Achievement Award (2017) and IBM Research Accomplishment Award (2009).

She holds 64 patents and has published 120 research papers, several book chapters, three edited books and a monograph on sparse modeling.

Current Students

Ivan Anokhin

PhD - Université de Montréal

Co-supervisor :

Samira Ebrahimi Kahou

ivan.anokhin@mila.quebec

rishika.bhagwatkar@mila.quebec

Rifat Arefin

PhD - Université de Montréal

Arjun Ashok

PhD - Université de Montréal

Co-supervisor :

Alexandre Drouin

arjun.ashok@mila.quebec

Master's Research - Université de Montréal

PhD - Université de Montréal

ethan.caballero@mila.quebec

Pietro Cagnasso

PhD - Concordia University

Principal supervisor :

pietro.cagnasso@mila.quebec

Mohammad Javad Darvishi Bayazi

Amin Darabi

PhD - Université de Montréal

amin.darabi@mila.quebec

Collaborating researcher - Université de Montréal

mj.darvishi92@gmail.com

Wagner Drew

Master's Research - Concordia University

Principal supervisor :

Mirco Ravanelli

drew.wagner@mila.quebec

nadhir.hassen@mila.quebec

Mojtaba Faramarzi

PhD - Université de Montréal

faramarm@mila.quebec

Parviz Haggi Mani

Independent visiting researcher - -

parviz.haggi@gmail.com

Nadhir Hassen

Collaborating researcher - Université de Montréal

Niklas Herbster

Research Intern - Université de Montréal

Co-supervisor :

Gauthier Gidel

niklas.herbster@gmail.com

prateek.humane@mila.quebec

Mai-Thi Ho

Master's Research - Université de Montréal

mai-thi.ho@mila.quebec

Prateek Humane

PhD

arnav-kumar.jain@mila.quebec

Nizar Islah

PhD - Université de Montréal

Principal supervisor :

Eilif B. Muller

nizar.islah@mila.quebec

PhD - Université de Montréal

PhD - Université de Montréal

ayush.kaushal@mila.quebec

Arian Khorasani

Collaborating researcher - Université de Montréal

arian.khorasani@mila.quebec

Neeraj Kumar

Collaborating Alumni - Université de Montréal

neeraj.kumar@mila.quebec

Gwen Legate

PhD - Concordia University

Principal supervisor :

gwendolyne.legate@mila.quebec

David Lemay

Master's Research - Université de Montréal

david.lemay@mila.quebec

amin.mansouri@mila.quebec

Amin Mansouri

Collaborating Alumni - Université de Montréal

Collaborating researcher

memariaa@mila.quebec

Andrei Mircea

PhD - Université de Montréal

Collaborating researcher - Université de Montréal

diganta.misra@mila.quebec

motahareh.pourrahimi@mila.quebec

Gabriela Moisescu-Pareja

Collaborating researcher - McGill University

Principal supervisor :

Doina Precup

moisescg@mila.quebec

Timothy Nest

PhD - Université de Montréal

Co-supervisor :

Eilif B. Muller

timothy.nest@mila.quebec

Jonathan Pilault

Collaborating researcher - Polytechnique Montréal

PhD - McGill University

Principal supervisor :

Pouya Bashivan

Mahta Ramezanian

Master's Research - Université de Montréal

Co-supervisor :

Guillaume Dumas

mahta.ramezanian@mila.quebec

Matthew Riemer

PhD - Université de Montréal

matthew.riemer@mila.quebec

Alexis Roger

PhD - McGill University

Principal supervisor :

Blake Richards

alexis.roger@mila.quebec

munish.sathish-kumar@mila.quebec

Munish Sathish Kumar

Collaborating researcher

Zibo Shang

PhD - Université de Montréal

zibo.shang@mila.quebec

Vaibhav Singh

PhD - Concordia University

Principal supervisor :

vaibhav.singh@mila.quebec

Gopeshh Subbaraj

PhD - Université de Montréal

gopeshh.subbaraj@mila.quebec

PhD - Université de Montréal

Co-supervisor :

benjamin.therien@mila.quebec

Collaborating researcher - Université de Montréal

tejas.vaidhya@mila.quebec

andrew.williams@mila.quebec

Sihui Wei

Undergraduate - McGill University

sihui.wei@mila.quebec

Andrew Williams

PhD - Université de Montréal

Co-supervisor :

Nicolas Chapados

daria.yasafova@mila.quebec

Daria Yasafova

Master's Research - Université de Montréal

He Zhu

PhD - McGill University

he.zhu@mila.quebec

Publications

GitChameleon 2.0: Evaluating AI Code Generation Against Python Library Version Incompatibilities

Victor May

Samira Ebrahimi Kahou

Massimo Caccia

The rapid evolution of software libraries poses a considerable hurdle for code generation, necessitating continuous adaptation to frequent v… (see more)ersion updates while preserving backward compatibility. While existing code evolution benchmarks provide valuable insights, they typically lack execution-based evaluation for generating code compliant with specific library versions. To address this, we introduce GitChameleon 2.0, a novel, meticulously curated dataset comprising 328 Python code completion problems, each conditioned on specific library versions and accompanied by executable unit tests. GitChameleon 2.0 rigorously evaluates the capacity of contemporary large language models (LLMs), LLM-powered agents, code assistants, and RAG systems to perform version-conditioned code generation that demonstrates functional accuracy through execution. Our extensive evaluations indicate that state-of-the-art systems encounter significant challenges with this task; enterprise models achieving baseline success rates in the 48-51% range, underscoring the intricacy of the problem. By offering an execution-based benchmark emphasizing the dynamic nature of code libraries, GitChameleon 2.0 enables a clearer understanding of this challenge and helps guide the development of more adaptable and dependable AI code generation methods. We make the dataset and evaluation code publicly available at https://github.com/mrcabbage972/GitChameleonBenchmark.

2025-12-31

Association for Computational Linguistics (published)

Position: Collusion Risks Among AI Reasoning Agents Justify Certification Requirements for Making Market Decisions

Matthew Riemer

Tommaso Tosato

Maximilian Puelma Touzel

This position paper argues that AI agents with chain-of-thought reasoning capabilities are predisposed to exhibit collusive behavior and sho… (see more)uld be required to obtain behavioral certification before making decisions that affect economic markets. This is because integrating these agents into society could collapse the legal evidentiary distinction between competition and collusion among independent firms without eroding the economic harm distinction. Experiments with DeepSeek-R1 agents in the Bertrand oligopoly pricing domain reveal a tendency towards tacit collusion that persists even when humans prompt the agents not to collude. We further show that the chain-of- thought of these agents can be steered toward either extremely collusive or highly competitive behavior in a way that is not semantically detectable by another LLM analyzing the reasoning traces. As a result, deploying reasoning agents for market decisions leads to collusive economic outcomes without any evidence of conspiracy or intent. Thus, certification based on observed behavior in representative situations is necessary to prevent collusion. We provide preliminary evidence that such agents can be steered in a generalizable way toward efficient competitive equilibria. However, developing a comprehensive behavioral certification will be required before these models can be deployed in real-world markets while ensuring their stability and efficiency.

2025-12-31

International Conference on Machine Learning (Accept (regular))

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 … (see more)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 (published)

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 (published)

arxiv.org

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… (see more)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 (preprint)

arxiv.org

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 … (see more)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 (published)

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 (accepted)

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… (see more)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 (published)

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 (preprint)

arxiv.org

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… (see more)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 (published)

MuLoCo: Muon is a practical inner optimizer for DiLoCo

2025-06-10

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