Antoine Lesage-Landry

Associate Academic Member

Associate Professor, Polytechnique Montréal, Department of Electrical Engineering

Research Topics

Online Learning

Optimization

Biography

I am an Associate professor in the Department of Electrical Engineering at Polytechnique Montréal. I received my BEng degree in engineering physics from Polytechnique Montréal in 2015, and my PhD degree in electrical engineering from the University of Toronto in 2019. I was a postdoctoral scholar in the Energy & Resources Group at the University of California, Berkeley, from 2019 to 2020. My research interests include optimization, online learning and machine learning, and their application to power systems with renewable generation.

Current Students

Yassine Afif

Master's Research - Polytechnique Montréal

Github

Xavier Audet

Master's Research - Polytechnique Montréal

Github

Mohamed Charara

Master's Research - Polytechnique Montréal

Co-supervisor :

Hanane Dagdougui

Marc-Antoine Coulombe

PhD - Université du Québec à Rimouski

Victor Darleguy

Master's Research - Polytechnique Montréal

Julie Durette

Master's Research - Polytechnique Montréal

Donald Fisher

Master's Research - Polytechnique Montréal

Github

Jérôme Lafontaine

Master's Research - Polytechnique Montréal

Github

Louis Lalonde

Master's Research - Polytechnique Montréal

Feng Li

PhD - Polytechnique Montréal

Samuel Mendoza

Master's Research - Polytechnique Montréal

Abraham N’Zi

PhD - Polytechnique Montréal

Julien Pallage

Master's Research - Polytechnique Montréal

Github

Christina G. Soldati

Master's Research - Polytechnique Montréal

Github

Chao Yin

PhD - Polytechnique Montréal

chao.yin@polymtl.ca

Maeva Zambou Zoleko

PhD - Polytechnique Montréal

Github

William Zhang

Master's Research - Polytechnique Montréal

Website

Github

Baptiste ZOGHBY

Research Intern - Polytechnique Montréal

Github

Publications

Sliced-Wasserstein Distance-based Data Selection

Julien Pallage

Antoine Lesage-Landry

2025-04-17

ArXiv (preprint)

arxiv.org

Min-Max Optimisation for Nonconvex-Nonconcave Functions Using a Random Zeroth-Order Extragradient Algorithm

Amir Ali Farzin

Yuen-Man Pun

Philipp Braun

Antoine Lesage-Landry

Youssef Diouane

Iman Shames

2025-04-10

ArXiv (preprint)

arxiv.org

Min-Max Optimisation for Nonconvex-Nonconcave Functions Using a Random Zeroth-Order Extragradient Algorithm

Amir Ali Farzin

Yuen-Man Pun

Philipp Braun

Antoine Lesage-Landry

Youssef Diouane

Iman Shames

2025-04-01

arXiv (published)

doi.org

arxiv.org

Online Interior-point Methods for Time-varying Equality-constrained Optimization

Jean-Luc Lupien

Iman Shames

Antoine Lesage-Landry

2025-04-01

IEEE Transactions on Automatic Control (published)

doi.org

arxiv.org

Sliced-Wasserstein Distance-based Data Selection

Julien Pallage

Antoine Lesage-Landry

We propose a new unsupervised anomaly detection method based on the sliced-Wasserstein distance for training data selection in machine learn… (see more)ing approaches. Our filtering technique is interesting for decision-making pipelines deploying machine learning models in critical sectors, e.g., power systems, as it offers a conservative data selection and an optimal transport interpretation. To ensure the scalability of our method, we provide two efficient approximations. The first approximation processes reduced-cardinality representations of the datasets concurrently. The second makes use of a computationally light Euclidian distance approximation. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We present the filtering patterns of our method on synthetic datasets and numerically benchmark our method for training data selection. Finally, we employ our method as part of a first forecasting benchmark for our open-source dataset.

2025-04-01

arXiv (published)

doi.org

arxiv.org

A Scalable Architecture for Future Regenerative Satellite Payloads

Olfa Ben Yahia

Zineb Garroussi

Brunilde Sansò

Jean-François Frigon

Stéphane Martel

Antoine Lesage-Landry

Gunes Karabulut Kurt

This paper addresses the limitations of current satellite payload architectures, which are predominantly hardware-driven and lack the flexib… (see more)ility to adapt to increasing data demands and uneven traffic. To overcome these challenges, we present a novel architecture for future regenerative and programmable satellite payloads and utilize interconnected modem banks to promote higher scalability and flexibility. We formulate an optimization problem to efficiently manage traffic among these modem banks and balance the load. Additionally, we provide comparative numerical simulation results, considering end-to-end delay and packet loss analysis. The results illustrate that our proposed architecture maintains lower delays and packet loss even with higher traffic demands and smaller buffer sizes.

2025-02-01

IEEE Wireless Communications Letters (published)

doi.org

arxiv.org

Ex Post Conditions for the Exactness of Optimal Power Flow Conic Relaxations

Jean-Luc Lupien

Antoine Lesage-Landry

Convex relaxations of the optimal power flow (OPF) problem provide an efficient alternative to solving the intractable alternating current (… (see more)AC) optimal power flow. The conic subset of OPF convex relaxations, in particular, greatly accelerate resolution while leading to high-quality approximations that are exact in several scenarios. However, the sufficient conditions guaranteeing exactness are stringent, e.g., requiring radial topologies. In this short communication, we present two equivalent ex post conditions for the exactness of any conic relaxation of the OPF. These rely on obtaining either a rank-1 voltage matrix or self-coherent cycles. Instead of relying on sufficient conditions a priori, satisfying one of the presented ex post conditions acts as an exactness certificate for the computed solution. The operator can therefore obtain an optimality guarantee when solving a conic relaxation even when a priori exactness requirements are not met. Finally, we present numerical examples from the MATPOWER library where the ex post conditions hold even though the exactness sufficient conditions do not, thereby illustrating the use of the conditions.

2025-01-01

Electric Power Systems Research (published)

doi.org

arxiv.org

Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates

Julien Pallage

Bertrand Scherrer

Salma Naccache

Christophe B'elanger

Antoine Lesage-Landry

In this work, we present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric. This filtering tech… (see more)nique is conceptually interesting for MLOps pipelines deploying machine learning models in critical sectors, e.g., energy, as it offers a conservative data selection. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We demonstrate the capabilities of our method on synthetic datasets as well as standard AD datasets and use it in the making of a first benchmark for our open-source localized critical peak rebate dataset.

2024-10-29

ArXiv (preprint)

doi.org

arxiv.org

Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates

Julien Pallage

Bertrand Scherrer

Salma Naccache

Christophe B'elanger

Antoine Lesage-Landry

2024-10-29

ArXiv (preprint)