Portrait of Foutse Khomh

Foutse Khomh

Associate Academic Member
Canada CIFAR AI Chair
Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering

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

Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal

Publications

Software-Engineering Design Patterns for Machine Learning Applications
Hironori Washizaki
Yann‐Gaël Guéhéneuc
Hironori Takeuchi
Naotake Natori
Takuo Doi
Satoshi Okuda
In this study, a multivocal literature review identified 15 software-engineering design patterns for machine learning applications. Findings… (see more) suggest that there are opportunities to increase the patterns’ adoption in practice by raising awareness of such patterns within the community.
Software-Engineering Design Patterns for Machine Learning Applications
Hironori Washizaki
Yann‐Gaël Guéhéneuc
Hironori Takeuchi
Naotake Natori
Takuo Doi
Satoshi Okuda
In this study, a multivocal literature review identified 15 software-engineering design patterns for machine learning applications. Findings… (see more) suggest that there are opportunities to increase the patterns’ adoption in practice by raising awareness of such patterns within the community.
On the Performance Implications of Deploying IoT Apps as FaaS
Mohab Aly
Soumaya Yacout
FIXME: synchronize with database! An empirical study of data access self-admitted technical debt
Biruk Asmare Muse
Csaba Zoltán Nagy
Anthony Cleve
Giuliano Antoniol
On the Performance Implications of Deploying IoT Apps as FaaS
M. Aly
Soumaya Yacout
Machine learning application development: practitioners’ insights
Md. Saidur Rahman
Alaleh Hamidi
Jinghui Cheng
Giuliano Antoniol
Hironori Washizaki
Faults in deep reinforcement learning programs: a taxonomy and a detection approach
Amin Nikanjam
Mohammad Mehdi Morovati
Houssem Ben Braiek
Clones in deep learning code: what, where, and why?
Hadhemi Jebnoun
Md. Saidur Rahman
Biruk Asmare Muse
Faults in deep reinforcement learning programs: a taxonomy and a detection approach
Amin Nikanjam
Mohammad Mehdi Morovati
Houssem Ben Braiek
Multi-language design smells: a backstage perspective
Mouna Abidi
Md. Saidur Rahman
Moses Openja
Multi-language design smells: a backstage perspective
Mouna Abidi
Moses Openja
Md Saidur Rahman