About
Appointed Canada CIFAR AI Chair – 2021
Foutse Khomh is a Canada CIFAR AI Chair at Mila, a professor at the Department of Computer Engineering and Software Engineering at Polytechnique Montréal and Scientific Co-Director – Scientific and International Activities at IVADO. He is also a Canada Research Chair Tier 1 on Trustworthy Intelligent Software Systems, and FRQ-IVADO Research Chair on Software Quality Assurance for Machine Learning Applications.
Khomh is a leader in Software Engineering (SE) with strong emphasis on the use of Machine Learning (ML) in supporting SE activities, and the use of SE to engineer world-class trustworthy ML systems. His work uniquely combines his deep knowledge on software quality and ML to tackle the multifaceted problem of engineering trustworthy ML-powered software systems. Khomh’s research activities contribute theories, methods, and tools to support the development, testing, and release of dependable and trustworthy ML-powered software systems. His work has received four ten-year Most Influential Paper (MIP) Awards, seven Best/Distinguished paper Awards at major conferences, and two Best Journal Paper of the Year Awards.
Awards
- Canada Research Chair Tier 1 on Trustworthy Intelligent Software Systems, 2024
- Honoris Genius Prize - Engineering Research or Teaching, 2024
- Journal of Systems and Software (JSS) Best Paper of the Year Award, 2024
- Arthur B. McDonald Fellowship, 2023
- IEEE Computer Society Best Paper Award, 2023
- ADRIQ-RSRI Innovation Award, 2022
- IEEE TCSE Most Influential Paper (MIP) Award, IEEE Computer Society, 2021.
- IEEE TCSE Distinguished Paper Award, IEEE Computer Society, 2020.
- Outstanding Young Computer Science Researcher Prize, CS-Can/Info-Can, 2020
- FRQ-IVADO Research Chair on Software Quality Assurance for Machine Learning Applications, FRQ, 2019
- IEEE TCSE Most Influential Paper (MIP) Award, IEEE Computer Society, 2019.
Relevant Publications
- Tambon, F., Khomh, F., & Antoniol, G. (2024). GIST: Generated inputs sets transferability in deep learning. Transactions on Software Engineering and Methodology (TOSEM), ACM.
- Laberge, G., Pequignot, Y. B., Marchand, M., & Khomh, F. (2024). Tackling the XAI disagreement problem with regional explanations. In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS).
- Openja, M., Laberge, G., & Khomh, F. (2024). Detection and evaluation of bias inducing features in machine learning. Journal of Empirical Software Engineering, 29, 22.
- Laberge, G., Pequignot, Y., Mathieu, A., Khomh, F., & Marchand, M. (2023). Partial order in chaos: Consensus on feature attributions in the Rashomon set. Journal of Machine Learning Research, 24.
- Laberge, G., Aïvodji, U., Hara, S., Marchand, M., & Khomh, F. (2023). Fooling SHAP with stealthily biased sampling. In Proceedings of the 11th International Conference on Learning Representations (ICLR).