Portrait of Foutse Khomh

Foutse Khomh

Associate Academic Member
Canada CIFAR AI Chair
Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering
Research Topics
Data Mining
Deep Learning
Distributed Systems
Generative Models
Learning to Program
Natural Language Processing
Reinforcement Learning
Website
Google Scholar
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Biography

Foutse Khomh is a full professor of software engineering at Polytechnique Montréal, a Canada CIFAR AI Chair – Trustworthy Machine Learning Software Systems, and an FRQ-IVADO Research Chair in Software Quality Assurance for Machine Learning Applications. Khomh completed a PhD in software engineering at Université de Montréal in 2011, for which he received an Award of Excellence. He was also awarded a CS-Can/Info-Can Outstanding Young Computer Science Researcher Prize in 2019.

His research interests include software maintenance and evolution, machine learning systems engineering, cloud engineering, and dependable and trustworthy ML/AI. His work has received four Ten-year Most Influential Paper (MIP) awards, and six Best/Distinguished Paper Awards. He has served on the steering committee of numerous organizations in software engineering, including SANER (chair), MSR, PROMISE, ICPC (chair), and ICSME (vice-chair). He initiated and co-organized Polytechnique Montréal‘s Software Engineering for Machine Learning Applications (SEMLA) symposium and the RELENG (release engineering) workshop series.

Khomh co-founded the NSERC CREATE SE4AI: A Training Program on the Development, Deployment and Servicing of Artificial Intelligence-based Software Systems, and is a principal investigator for the DEpendable Explainable Learning (DEEL) project.

He also co-founded Confiance IA, a Quebec consortium focused on building trustworthy AI, and is on the editorial board of multiple international software engineering journals, including IEEE Software, EMSE and JSEP. He is a senior member of IEEE.

Current Students

Nanda Assobjio Brice Yvan
Collaborating Alumni - Polytechnique Montréal
Google Scholar
Gabriel Laberge
PhD - Polytechnique Montréal
Github
Google Scholar
forough majidi
PhD - Polytechnique Montréal
Website
Google Scholar
Mo Malekpour
Master's Research - Polytechnique Montréal
mohammadhossein.malekpour@gmail.com
Website
Github
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Mohamed Amine Merzouk
Postdoctorate - Polytechnique Montréal
Co-supervisor :
Sarath Chandar
Website
Github
Google Scholar
Khouloud Oueslati
Master's Research - Polytechnique Montréal
Elnathan Tiokou Tiokou Fangang
PhD - Polytechnique Montréal
Github
Ben Braiek Yasmine
Master's Research - Polytechnique Montréal

Publications

SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs
Roozbeh Aghili
Xingfang Wu
Foutse Khomh
Heng Li
2025-05-20
ArXiv (preprint)
arxiv.org
arxiv.org
SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs
Roozbeh Aghili
Xingfang Wu
Foutse Khomh
Heng Li
2025-05-20
ArXiv (preprint)
arxiv.org
arxiv.org
Kernel-Level Event-Based Performance Anomaly Detection in Software Systems under Varying Load Conditions
Anthonia Njoku
Heng Li
Foutse Khomh
2025-05-05
Companion of the 16th ACM/SPEC International Conference on Performance Engineering (published)
doi.org
Logging requirement for continuous auditing of responsible machine learning-based applications
Patrick Loic Foalem
Leuson Da Silva
Foutse Khomh
Heng Li
Ettore Merlo
2025-04-14
Empirical Software Engineering (published)
doi.org
Leveraging Machine Learning Techniques in Intrusion Detection Systems for Internet of Things
Saeid Jamshidi
Amin Nikanjam
Nafi Kawser Wazed
Foutse Khomh
2025-04-09
ArXiv (preprint)
arxiv.org
arxiv.org
Prism: Dynamic and Flexible Benchmarking of LLMs Code Generation with Monte Carlo Tree Search
Vahid Majdinasab
Amin Nikanjam
Foutse Khomh
2025-04-07
ArXiv (preprint)
arxiv.org
arxiv.org
Evaluating and Enhancing Segmentation Model Robustness with Metamorphic Testing
Seif Mzoughi
Mohamed Elshafeia
Foutse Khomh
2025-04-03
ArXiv (preprint)
arxiv.org
arxiv.org
Towards Assessing Deep Learning Test Input Generators
Seif Mzoughi
Ahmed Haj Yahmed
Mohamed Elshafei
Foutse Khomh
Diego Elias Costa
2025-04-03
ArXiv (preprint)
arxiv.org
arxiv.org
Evaluating and Enhancing Segmentation Model Robustness with Metamorphic Testing
Seif Mzoughi
Mohamed Elshafeia
Foutse Khomh
2025-04-01
arXiv (published)
doi.org
arxiv.org
Leveraging Machine Learning Techniques in Intrusion Detection Systems for Internet of Things
Saeid Jamshidi
Amin Nikanjam
Kawser Wazed Nafi
Foutse Khomh
As the Internet of Things (IoT) continues to expand, ensuring the security of connected devices has become increasingly critical. Traditiona… (see more)l Intrusion Detection Systems (IDS) often fall short in managing the dynamic and large-scale nature of IoT networks. This paper explores how Machine Learning (ML) and Deep Learning (DL) techniques can significantly enhance IDS performance in IoT environments. We provide a thorough overview of various IDS deployment strategies and categorize the types of intrusions common in IoT systems. A range of ML methods -- including Support Vector Machines, Naive Bayes, K-Nearest Neighbors, Decision Trees, and Random Forests -- are examined alongside advanced DL models such as LSTM, CNN, Autoencoders, RNNs, and Deep Belief Networks. Each technique is evaluated based on its accuracy, efficiency, and suitability for real-world IoT applications. We also address major challenges such as high false positive rates, data imbalance, encrypted traffic analysis, and the resource constraints of IoT devices. In addition, we highlight the emerging role of Generative AI and Large Language Models (LLMs) in improving threat detection, automating responses, and generating intelligent security policies. Finally, we discuss ethical and privacy concerns, underscoring the need for responsible and transparent implementation. This paper aims to provide a comprehensive framework for developing adaptive, intelligent, and secure IDS solutions tailored for the evolving landscape of IoT.
2025-04-01
arXiv (published)
doi.org
arxiv.org
Performance Smells in ML and Non-ML Python Projects: A Comparative Study
Franccois Belias
Leuson Da Silva
Foutse Khomh
Cyrine Zid
2025-04-01
arXiv (published)
doi.org
arxiv.org
Prism: Dynamic and Flexible Benchmarking of LLMs Code Generation with Monte Carlo Tree Search
Vahid Majdinasab
Amin Nikanjam
Foutse Khomh
2025-04-01
arXiv (published)
doi.org
arxiv.org