Portrait of Antoine Lesage-Landry

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

Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Co-supervisor :
PhD - Université du Québec à Rimouski
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
PhD - Polytechnique Montréal
Master's Research - Polytechnique Montréal
PhD - Polytechnique Montréal
Master's Research - Polytechnique Montréal
PhD - Polytechnique Montréal
PhD - Polytechnique Montréal
PhD - Polytechnique Montréal
Master's Research - Polytechnique Montréal

Publications

Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates
Bertrand Scherrer
Salma Naccache
Christophe B'elanger
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.
Inferring electric vehicle charging patterns from smart meter data for impact studies
Élodie Campeau
Ilhan Kocar
Inferring electric vehicle charging patterns from smart meter data for impact studies
Élodie Campeau
Ilhan Kocar
Inferring electric vehicle charging patterns from smart meter data for impact studies
Élodie Campeau
Ilhan Kocar
Online Convex Optimization for On-Board Routing in High-Throughput Satellites
Olivier B'elanger
Jean-Luc Lupien
Olfa Ben Yahia
Stéphane Martel
Gunes Karabulut Kurt
The rise in low Earth orbit (LEO) satellite Internet services has led to increasing demand, often exceeding available data rates and comprom… (see more)ising the quality of service. While deploying more satellites offers a short-term fix, designing higher-performance satellites with enhanced transmission capabilities provides a more sustainable solution. Achieving the necessary high capacity requires interconnecting multiple modem banks within a satellite payload. However, there is a notable gap in research on internal packet routing within extremely high-throughput satellites. To address this, we propose a real-time optimal flow allocation and priority queue scheduling method using online convex optimization-based model predictive control. We model the problem as a multi-commodity flow instance and employ an online interior-point method to solve the routing and scheduling optimization iteratively. This approach minimizes packet loss and supports real-time rerouting with low computational overhead. Our method is tested in simulation on a next-generation extremely high-throughput satellite model, demonstrating its effectiveness compared to a reference batch optimization and to traditional methods.
Wasserstein Distributionally Robust Shallow Convex Neural Networks
Wasserstein Distributionally Robust Shallow Convex Neural Networks
In this work, we propose Wasserstein distributionally robust shallow convex neural networks (WaDiRo-SCNNs) to provide reliable nonlinear pre… (see more)dictions when subject to adverse and corrupted datasets. Our approach is based on a new convex training program for
A Rapid Method for Impact Analysis of Grid-Edge Technologies on Power Distribution Networks
This paper presents a novel rapid estimation method (REM) to perform stochastic impact analysis of grid-edge technologies (GETs) to the powe… (see more)r distribution networks. The evolution of network states' probability density functions (PDFs) in terms of GET penetration levels are characterized by the Fokker-Planck equation (FPE). The FPE is numerically solved to compute the PDFs of network states, and a calibration process is also proposed such that the accuracy of the REM is maintained for large-scale distribution networks. The approach is illustrated on a large-scale realistic distribution network using a modified version of the IEEE 8500 feeder, where electric vehicles (EVs) or photovoltaic systems (PVs) are installed at various penetration rates. It is demonstrated from quantitative analyses that the results from our proposed approach have negligible errors comparing with those obtained from Monte Carlo simulations.
A Scalable Architecture for Future Regenerative Satellite Payloads
Olfa Ben Yahia
Zineb Garroussi
Brunilde Sansò
Jean-François Frigon
Stéphane Martel
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.
A Scalable Architecture for Future Regenerative Satellite Payloads
Olfa Ben Yahia
Zineb Garroussi
Brunilde Sansò
Jean-François Frigon
Stéphane Martel
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.
A Scalable Architecture for Future Regenerative Satellite Payloads
Olfa Ben Yahia
Zineb Garroussi
Brunilde Sansò
Jean-François Frigon
Stéphane Martel
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.
Tensor-based Space Debris Detection for Satellite Mega-constellations
Olivier Daoust
Hasan Nayir
Irfan Azam
Gunes Karabulut Kurt