Portrait of Samira Ebrahimi Kahou

Samira Ebrahimi Kahou

Affiliate Member
Associate Professor, University of Calgary, Deparment of Electrical and Software Engineering
Adjunct Professor, École de technologie suprérieure, School of Computer Science
Adjunct Professor, McGill University, School of Computer Science
Research Topics
Computer Vision
Deep Learning
Medical Machine Learning
Multimodal Learning
Natural Language Processing
Reinforcement Learning
Representation Learning
Website
Google Scholar
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Biography

Samira is an Associate Professor at the University of Calgary in the Schulich School of Engineering. She is also an Adjunct Professor at École de technologie supérieure (ÉTS) in the Department of Software Engineering and Information Technology and McGill University in the School of Computer Science. She is an academic member of Mila - Québec Artificial Intelligence Institute and holds a Canada CIFAR AI Chair. Samira received her Ph.D. in Computer Engineering from Polytechnique Montréal/Mila with an award for the best thesis in the department. Samira also worked as a Postdoctoral Fellow at the McGill School of Computer Science and as a Researcher at Microsoft Research Montréal.

Samira and her group work on solving fundamental problems in representation learning for decision making, with a broad focus on explainability, generalization and efficient learning. Her work has been published in top-tier venues, such as NeurIPS, ICLR, ICML, ICCV, CVPR, TMLR and CoRL. Samira received the 2024 Early Career Excellence in Research Award from the Schulich School of Engineering. Her impactful work in multi-modal learning was recognized twice by ACM ICMI Ten-Year Technical Impact Awards, 2023 as Runner-up and 2025 as Winner.

Current Students

Ivan Anokhin
PhD - Université de Montréal
Principal supervisor :
Irina Rish
Github
Google Scholar
Meghana Bhange
PhD - École de technologie suprérieure
Principal supervisor :
Ulrich Aivodji
Website
Github
Google Scholar
Patrik Kenfack
PhD - École de technologie suprérieure
Principal supervisor :
Ulrich Aivodji
Website
Github
Google Scholar
Somjit Nath
PhD - McGill University
Co-supervisor :
Derek Nowrouzezahrai
Website
Google Scholar
Priyesh Vijayan
PhD - McGill University
Principal supervisor :
Doina Precup
Website
Github
Google Scholar

Publications

Survey on <scp>AI</scp> Ethics: A Socio‐Technical Perspective
Dave Mbiazi
Meghana Bhange
Maryam Babaei
Ivaxi Sheth
Patrik Joslin Kenfack
Samira Ebrahimi Kahou
Abstract The past decade has observed a significant advancement in AI, with deep learning‐based models being deployed in diverse scenarios… (see more), including safety‐critical applications. As these AI systems become deeply embedded in our societal infrastructure, the repercussions of their decisions and actions have significant consequences, making the ethical implications of AI deployment highly relevant and essential. The ethical concerns associated with AI are multifaceted, including challenging issues of fairness, privacy and data protection, responsibility and accountability, safety and robustness, transparency and explainability, and environmental impact. These principles together form the foundations of ethical AI considerations that concern every stakeholder in the AI system lifecycle. In light of the present ethical and future x‐risk concerns, governments have shown increasing interest in establishing guidelines for the ethical deployment of AI. This work unifies the current and future ethical concerns of deploying AI into society. While we acknowledge and appreciate the technical surveys for each of the ethical principles concerned, in this paper, we aim to provide a comprehensive overview that not only addresses each principle from a technical point of view but also discusses them from a social perspective.
2025-11-01
Computational Intelligence (published)
doi.org
arxiv.org
Attention-Based Multi-Agent RL for Multi-Machine Tending Using Mobile Robots
Abdalwhab Abdalwhab
Giovanni Beltrame
Samira Ebrahimi Kahou
David St-Onge
Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborat… (see more)ive robots can tackle that can also greatly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. We introduce a multi-agent multi-machine-tending learning framework using mobile robots based on multi-agent reinforcement learning (MARL) techniques, with the design of a suitable observation and reward. Moreover, we integrate an attention-based encoding mechanism into the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine-tending scenarios. Our model (AB-MAPPO) outperforms MAPPO in this new challenging scenario in terms of task success, safety, and resource utilization. Furthermore, we provided an extensive ablation study to support our design decisions.
2025-10-01
Applied Informatics (published)
doi.org
Attention-Based Multi-Agent RL for Multi-Machine Tending Using Mobile Robots
Abdalwhab Bakheet Mohamed Abdalwhab
Giovanni Beltrame
Samira Ebrahimi Kahou
David St-Onge
2025-10-01
AI (published)
doi.org
Attention-Based Multi-Agent RL for Multi-Machine Tending Using Mobile Robots
Abdalwhab Abdalwhab
Giovanni Beltrame
Samira Ebrahimi Kahou
David St-Onge
Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborat… (see more)ive robots can tackle that can also greatly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. We introduce a multi-agent multi-machine-tending learning framework using mobile robots based on multi-agent reinforcement learning (MARL) techniques, with the design of a suitable observation and reward. Moreover, we integrate an attention-based encoding mechanism into the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine-tending scenarios. Our model (AB-MAPPO) outperforms MAPPO in this new challenging scenario in terms of task success, safety, and resource utilization. Furthermore, we provided an extensive ablation study to support our design decisions.
2025-10-01
AI (published)
doi.org
Behaviour Discovery and Attribution for Explainable Reinforcement Learning
Rishav
Somjit Nath
Vincent Michalski
Samira Ebrahimi Kahou
2025-09-02
TMLR (accepted)
openreview.net
openreview.net
Zero-Shot Anomaly Detection with Dual-Branch Prompt Learning
Zihan Wang
Samira Ebrahimi Kahou
Narges Armanfard
Zero-shot anomaly detection (ZSAD) enables identifying and localizing defects in unseen categories by relying solely on generalizable featur… (see more)es rather than requiring any labeled examples of anomalies. However, existing ZSAD methods, whether using fixed or learned prompts, struggle under domain shifts because their training data are derived from limited training domains and fail to generalize to new distributions. In this paper, we introduce PILOT, a framework designed to overcome these challenges through two key innovations: (1) a novel dual-branch prompt learning mechanism that dynamically integrates a pool of learnable prompts with structured semantic attributes, enabling the model to adaptively weight the most relevant anomaly cues for each input image; and (2) a label-free test-time adaptation strategy that updates the learnable prompt parameters using high-confidence pseudo-labels from unlabeled test data. Extensive experiments on 13 industrial and medical benchmarks demonstrate that PILOT achieves state-of-the-art performance in both anomaly detection and localization under domain shift.
2025-08-01
ArXiv (preprint)
arxiv.org
arxiv.org
Zero-Shot Anomaly Detection with Dual-Branch Prompt Selection
Zihan Wang
Samira Ebrahimi Kahou
Narges Armanfard
Zero-shot anomaly detection (ZSAD) enables identifying and localizing defects in unseen categories by relying solely on generalizable featur… (see more)es rather than requiring any labeled examples of anomalies. However, existing ZSAD methods, whether using fixed or learned prompts, struggle under domain shifts because their training data are derived from limited training domains and fail to generalize to new distributions. In this paper, we introduce PILOT, a framework designed to overcome these challenges through two key innovations: (1) a novel dual-branch prompt learning mechanism that dynamically integrates a pool of learnable prompts with structured semantic attributes, enabling the model to adaptively weight the most relevant anomaly cues for each input image; and (2) a label-free test-time adaptation strategy that updates the learnable prompt parameters using high-confidence pseudo-labels from unlabeled test data. Extensive experiments on 13 industrial and medical benchmarks demonstrate that PILOT achieves state-of-the-art performance in both anomaly detection and localization under domain shift.
2025-08-01
ArXiv (preprint)
arxiv.org
arxiv.org
Zero-Shot Anomaly Detection with Dual-Branch Prompt Selection
Zihan Wang
Samira Ebrahimi Kahou
Narges Armanfard
Zero-shot anomaly detection (ZSAD) enables identifying and localizing defects in unseen categories by relying solely on generalizable featur… (see more)es rather than requiring any labeled examples of anomalies. However, existing ZSAD methods, whether using fixed or learned prompts, struggle under domain shifts because their training data are derived from limited training domains and fail to generalize to new distributions. In this paper, we introduce PILOT, a framework designed to overcome these challenges through two key innovations: (1) a novel dual-branch prompt learning mechanism that dynamically integrates a pool of learnable prompts with structured semantic attributes, enabling the model to adaptively weight the most relevant anomaly cues for each input image; and (2) a label-free test-time adaptation strategy that updates the learnable prompt parameters using high-confidence pseudo-labels from unlabeled test data. Extensive experiments on 13 industrial and medical benchmarks demonstrate that PILOT achieves state-of-the-art performance in both anomaly detection and localization under domain shift.
2025-08-01
arXiv (published)
doi.org
arxiv.org
GitChameleon: Evaluating AI Code Generation Against Python Library Version Incompatibilities
Diganta Misra
Nizar Islah
Victor May
Brice Rauby
Zihan Wang
Justine Gehring
Antonio Orvieto
Muawiz Chaudhary
Eilif Benjamin Muller
Irina Rish
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, a novel, meticulously curated dataset comprising 328 Python code completion problems, each conditioned on specific library versions and accompanied by executable unit tests. GitChameleon 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 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.
2025-07-16
ArXiv (preprint)
arxiv.org
arxiv.org
GitChameleon: Evaluating AI Code Generation Against Python Library Version Incompatibilities
Diganta Misra
Nizar Islah
Victor May
Brice Rauby
Zihan Wang
Justine Gehring
Antonio Orvieto
Muawiz Chaudhary
Eilif Benjamin Muller
Irina Rish
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, a novel, meticulously curated dataset comprising 328 Python code completion problems, each conditioned on specific library versions and accompanied by executable unit tests. GitChameleon 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 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.
2025-07-16
ArXiv (preprint)
arxiv.org
arxiv.org
GitChameleon: Evaluating AI Code Generation Against Python Library Version Incompatibilities
Diganta Misra
Nizar Islah
Victor May
Brice Rauby
Zihan Wang
Justine Gehring
Antonio Orvieto
Muawiz Chaudhary
Eilif Benjamin Muller
Irina Rish
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, a novel, meticulously curated dataset comprising 328 Python code completion problems, each conditioned on specific library versions and accompanied by executable unit tests. GitChameleon 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 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.
2025-07-16
ArXiv (preprint)
arxiv.org
arxiv.org
GitChameleon 2.0: Evaluating AI Code Generation Against Python Library Version Incompatibilities
Diganta Misra
Nizar Islah
Victor May
Brice Rauby
Zihan Wang
Justine Gehring
Antonio Orvieto
Muawiz Chaudhary
Eilif Benjamin Muller
Irina Rish
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.
2025-07-01
arXiv (published)
doi.org