Antoine Lesage-Landry
B.Eng. (Poly), Ph.D. (Toronto)
Associate Professor
Department of Electrical Engineering
Department of Electrical Engineering
Research interests and affiliations
Research interests
- Modelling of power systems
- Decision-making under uncertainty
- Optimization
- Online optimization
- Online learning
- Machine learning
- Electric energy systems
- Renewable energy systems
- Smart grids
Affiliation(s)
- Réseau québécois sur l'énergie intelligente, Member
- Institute for Data Valorization (IVADO), Member
- Advanced Research Centre in Microwaves and Space Electronics (POLY-GRAMES), Member
- Research Group in Decision Analysis (GERAD), Member
- Mila - Quebec's Artificial Intelligence Institute, Associate member
Expertise type(s) (NSERC subjects)
- 2501 Power systems
- 2715 Optimization
- 2805 Learning and inference theories
- 2956 Optimization and optimal control theory
- 2960 Mathematical modelling
Publications
Recent publications
Journal article
Report
Journal article
Report
Almekhlafi, M., Lesage-Landry, A., & Karabulut Kurt, G. (2025). Access Inequality in LEO Satellite Networks: A Case Study of High-Latitude Coverage in Northern Québec. IEEE Open Journal of Vehicular Technology, 1-17.
Audet, X., Qako, K., & Lesage-Landry, A. (2025). A distributionally robust optimization strategy for virtual bidding in two-settlement electricity markets. (Technical Report n° G-2025-31).
Audet, X., Qako, K., & Lesage-Landry, A. (2025). A Distributionally Robust Optimization Strategy for Virtual Bidding in Two-Settlement Electricity Markets. Sustainable Energy, Grids and Networks, 101904.
Soldati, C., Le Digabel, S., & Lesage-Landry, A. (2025). Blackbox optimization for loss minimization in power distribution networks using feeder reconfiguration. (Technical Report n° G-2025-52).
See all publications (59)
Biography
Antoine Lesage-Landry is an Associate Professor in the Department of Electrical Engineering at Polytechnique Montréal, QC, Canada. He received the B.Eng. degree in Engineering Physics from Polytechnique Montréal, in 2015, and the Ph.D. degree in Electrical Engineering from the University of Toronto, ON, Canada, in 2019. From 2019 to 2020, he was a Postdoctoral Scholar in the Energy & Resources Group at the University of California, Berkeley, CA, USA. His research interests include optimization, online learning, and their application to power systems with renewable generation.
Education
- Ph.D., Electrical Engineering, University of Toronto
- B.Eng., Engineering Physics, Polytechnique Montréal
Supervision at Polytechnique
COMPLETED
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Ph.D. Thesis (1)
- Li, F. (2024). Analysis and Optimization of Power Distribution Networks with High Penetration of Grid-Edge Technologies Under Uncertainty [Ph.D. thesis, Polytechnique Montréal].
- Li, F. (2024). Analysis and Optimization of Power Distribution Networks with High Penetration of Grid-Edge Technologies Under Uncertainty [Ph.D. thesis, Polytechnique Montréal].
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Master's Thesis (8)
- Pallage, J. (2025). Contributions to the Trustworthy Machine Learning Pipeline: Data Selection, Training, and Post-training Verification through Convexity and the Wasserstein Distance [Master's thesis, Polytechnique Montréal].
- Bélanger, O. (2024). Online Convex Optimization for On-Board Routing in High-Throughput Satellites [Master's thesis, Polytechnique Montréal].
- Mendoza, S. (2024). Répartition computationnelle efficace entre boîte noire et solveur [Master's thesis, Polytechnique Montréal].
- Molénat, M. C. (2024). Étude de la performance de la méthode nodale augmentée et modifiée pour les calculs d'écoulement de puissance [Master's thesis, Polytechnique Montréal].
- Lauzon, J.-W. (2023). A Discrete-Time Markov Chain Approach for Microgrid-Aware Reliability Assessment of Distribution Systems [Master's thesis, Polytechnique Montréal].
- Lupien, J.-L. (2023). Online Second-order Methods for Time-Varying Equality-Constrained Optimization [Master's thesis, Polytechnique Montréal].
- Maisonneuve, P. (2023). Learning-Accelerated Exact Mixed-Integer Second-Order Cone Programming for Unit Commitment [Master's thesis, Polytechnique Montréal].
- Paré, M.-C. (2023). Commande prédictive efficace guidée par les données pour la gestion de la demande de puissance des petits bâtiments commerciaux [Master's thesis, Polytechnique Montréal].
- Pallage, J. (2025). Contributions to the Trustworthy Machine Learning Pipeline: Data Selection, Training, and Post-training Verification through Convexity and the Wasserstein Distance [Master's thesis, Polytechnique Montréal].
