Maxime Lamothe
Ph.D. (Concordia), M.Eng. (Concordia), B.Eng. (McGill)
Assistant Professor
Department of Computer Engineering and Software Engineering
Department of Computer Engineering and Software Engineering
Areas of expertise
Logic programming Other computing methods Information systems design Software engineering Computer systems software Software and development
Logic programming Other computing methods Information systems design Software engineering Computer systems software Software and development
Primary sphere of excellence in research
New Frontiers in Information and Communication Technologies
New Frontiers in Information and Communication Technologies
Secondary sphere(s) of excellence in research
Modeling and Artificial Intelligence Industry of the Future and Digital Society
Modeling and Artificial Intelligence Industry of the Future and Digital Society
Research interests and affiliations
Research interests
- Empirical software engineering
- Mining software repositories
- Software APIs
- Software build systems
- Software performance
- Software bug-detection
Expertise type(s) (NSERC subjects)
- 2705 Software and development
- 2706 Software engineering
Publications
Recent publications
Conference paper
Dataset
Conference paper
Conference paper
Qin, Q., Li, H., Merlo, E., & Lamothe, M. (2025, April). Automated, Unsupervised, and Auto-Parameterized Inference of Data Patterns and Anomaly Detection [Paper]. 47th International Conference on Software Engineering (ICSE 2025), Ottawa, ON, Canada.
Qin, Q., Li, H., Merlo, E., & Lamothe, M. (2025). Automated, Unsupervised, and Auto-parameterized Inference of Data Patterns and Anomaly Detection [Dataset]. 
Ekhlasi, M., Prakash, A., Lamothe, M., & Dagenais, M. (2025, April). InsightAI: Root Cause Analysis in Large Log Files with Private Data Using Large Language Model [Paper]. 4th International Conference on AI Engineering – Software Engineering for AI (CAIN 2025), Ottawa, ON, Canada.
Kazemi, F., Lamothe, M., & McIntosh, S. (2025, September). Interrogative Comments Posed by Review Comment Generators: An Empirical Study of Gerrit [Paper]. International Symposium on Empirical Software Engineering and Measurement (ESEM 2025), Honolulu, HI, USA (12 pages).
See all publications (30)
Biography
Maxime Lamothe is an assistant professor at Polytechnique Montreal and is currently looking for master’s and Ph.D. students. Maxime studied software build systems as a postdoctoral researcher in the Software REBELs Lab at the University of Waterloo. His doctoral thesis, conducted in the SENSE Lab at Concordia University, focused on reducing knowledge gaps between the users and developers of software APIs. He obtained his Ph.D. from Concordia University (2020), M. Eng. degree from Concordia University (2017), and B. Eng. from McGill University (2013).
Teaching
- LOG3430 - Méthodes de test et de validation du logiciel
- LOG6305 - Techniques avancées de test du logiciel
Supervision at Polytechnique
COMPLETED
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Ph.D. Thesis (2)
- Darche, S. (2025). Profiling and Tracing Tools for General-Purpose Computing on Graphics Processing Units (GPGPU) [Ph.D. thesis, Polytechnique Montréal].
- Ekhlasi, M. (2025). Diagnostic automatisé des performances dans les systèmes distribués à l'aide de traces, de métriques système et de modèles de langage de grande taille [Ph.D. thesis, Polytechnique Montréal].
- Darche, S. (2025). Profiling and Tracing Tools for General-Purpose Computing on Graphics Processing Units (GPGPU) [Ph.D. thesis, Polytechnique Montréal].
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Master's Thesis (3)
- Nouriinanloo, B. (2024). Improving Information Retrieval and Recommender Systems with Contextual Data and Re-Ranking [Master's thesis, Polytechnique Montréal].
- Bouziane, A. (2023). On-Demand Health Data Provisioning with Custom Temporary Data Views for Big Data Platforms [Master's thesis, Polytechnique Montréal].
- Ghadesi, A. (2023). An Empirical Study of the Causes and Symptoms of Machine Learning Exceptions [Master's thesis, Polytechnique Montréal].
- Nouriinanloo, B. (2024). Improving Information Retrieval and Recommender Systems with Contextual Data and Re-Ranking [Master's thesis, Polytechnique Montréal].
