
Registered user since Mon 24 Jul 2017
Foutse Khomh is a Full Professor, a Canada Research Chair Tier 1, a Canada CIFAR AI Chair, an FRQ-IVADO Research Chair, an Honoris Genius Prize Laureate, and an NSERC Arthur B. McDonald Fellow at Polytechnique Montréal, where he heads the SWAT Lab (http://swat.polymtl.ca/). He received a Ph.D. in Software Engineering from the University of Montreal in 2011. His research interests include software maintenance and evolution, cloud engineering, machine learning systems engineering, empirical software engineering, software analytics, and dependable and trustworthy AI/ML. He has published over 200 conferences and journal papers. His work has received four ten-year Most Influential Paper (MIP) Awards, and eight Best/Distinguished Paper Awards. He has served on the program committees of several international conferences including ICSE, FSE, ICSM(E), SANER, MSR, ICPC, SCAM, ESEM and has reviewed for top international journals such as SQJ, JSS, EMSE, TSE, and TOSEM. He is program chair for Satellite Events at SANER 2015, program co-chair of SCAM 2015, ICSME 2018, PROMISE 2019, ICPC 2019, and SSBSE 2024, and general chair of ICPC 2018, SCAM 2020, and general co-chair of SANER 2020. He initiated and co-organized the Software Engineering for Machine Learning Applications (SEMLA) symposium. He is one of the organizers of the RELENG workshop series (http://releng.polymtl.ca) and Associate Editor for IEEE Software, EMSE, and JSEP.
Contributions
2026
ICSE
- FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks
- Towards Understanding the Impact of Data Bugs on Deep Learning Models in Software Engineering
- Imitation Game: Reproducing Deep Learning Bugs Leveraging an Intelligent Agent
- PC Member in ACM Student Research Competition within the SRC - ACM Student Research Competition-track
- An Efficient Model Maintenance Approach for MLOps
- Committee Member in Program Committee within the Research Track-track
- Logging requirement for continuous auditing of responsible machine learning-based applications
- RefAgent: A Multi-agent LLM-based Framework for Automatic Software Refactoring