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

Yassine Afif
Master's Research - Polytechnique Montréal
Mohamad Aziz
Postdoctorate - Polytechnique Montréal
Co-supervisor :
Hanane Dagdougui
Google Scholar
Marc-Antoine Coulombe
PhD - Université de Sherbrooke
Google Scholar
Jérôme Lafontaine
Master's Research - Polytechnique Montréal
Louis Lalonde
Master's Research - Polytechnique Montréal
Hannah Merrigan
PhD - Polytechnique Montréal
Abraham N’Zi
PhD - Polytechnique Montréal
Laure-Anne Réau
Master's Research - Polytechnique Montréal
Github
Christina G. Soldati
PhD - Polytechnique Montréal
Noureddine Toumi Toumi
Postdoctorate - Polytechnique Montréal
Theodoros Xenakis
Research Intern - Polytechnique Montréal
Website
Github
Chao Yin
PhD - Polytechnique Montréal
Maeva Zambou Zoleko
PhD - Polytechnique Montréal
Github
William Zhang
Master's Research - Polytechnique Montréal
Website
Github

Publications

Online Sketched Newton-Raphson
Jean-Luc Lupien
Yuen-Man Pun
Youssef Diouane
Iman Shames
Antoine Lesage-Landry
In online convex optimization (OCO), a decision-maker is confronted with an unknown environment and seeks to play an optimal sequence of dec… (see more)isions on a short time-scale using only past information. Recent advances in second-order OCO methods have demonstrated tighter regret bounds and improved empirical performance over traditional first-order methods. However, this performance comes at a cost: a matrix inversion is now required, which scales with the cube of the size of the problem. In this work, we propose sketching to mitigate this limitation. Specifically, we present the online sketched Newton-Raphson method (OSNR) which preserves the tight regret bounds obtained with second-order methods while presenting a strict computational improvement in terms of complexity. We discuss three application scenarios of OSNR: online root finding, unconstrained OCO, and time-varying equality-constrained OCO, and present their respective regret and a constraint violation bound for the latter. In all three applications, OSNR achieves sublinear dynamic regret bounds. For the equality-constrained case, the extension OSNR with equality constraints OSNR-EC is shown to yield sublinear cumulative constraint violation. Finally, we illustrate the performance of OSNR and OSNR-EC on two numerical examples, viz., online position tracking and optimal power flow, and observe that OSNR and OSNR-EC exhibit high performance even at low sampling rates.
2026-05-26
arXiv (preprint)
doi.org
arxiv.org
PIKAN: Physics-Inspired Kolmogorov-Arnold Networks for Explainable UAV Channel Modelling
Kürşat Tekbıyık
Gunes Karabulut Kurt
Antoine Lesage-Landry
Unmanned aerial vehicle (UAV) communications demand accurate yet interpretable air-to-ground (A2G) channel models that can adapt to non-stat… (see more)ionary propagation environments. While deterministic models offer interpretability and deep learning (DL) models provide accuracy, both approaches suffer from either rigidity or a lack of explainability. To bridge this gap, we propose the Physics-Inspired Kolmogorov-Arnold Network (PIKAN) that embeds physical principles (e.g., free-space path loss, two-ray reflections) into the learning process. Unlike physics-informed neural networks (PINNs), PIKAN is more flexible for applying physical information because it introduces them as adaptable inductive biases. Thus, it enables a more flexible training process. Experiments on UAV A2G measurement data show that PIKAN achieves comparable accuracy to DL models while providing symbolic and explainable expressions aligned with propagation laws. Remarkably, PIKAN achieves this performance with only 232 parameters, making it up to 37 times lighter than multilayer perceptron (MLP) baselines with thousands of parameters, without sacrificing correlation with measurements and also providing symbolic expressions. These results highlight PIKAN as an efficient, interpretable, and scalable solution for UAV channel modelling in beyond-5G and 6G networks.
2026-03-06
IEEE Aerospace Conference (published)
doi.org
arxiv.org
Practical Solutions to Volt-var Optimization under Uncertainty via Blackbox Optimization
Feng Li
Ilhan Kocar
Antoine Lesage-Landry
In this work, we propose an optimal reactive power dispatch (ORPD) stochastic program for volt-var optimization (VVO) of power distribution … (see more)networks. The formulation considers not only control settings of conventional VVO devices, e.g., voltage regulators, capacitor banks, and on-load tap changers, but also optimal settings for volt-var droop curves of distributed energy resources (DERs), compliant with the IEEE 1547-2018 standard. Instead of including the power flow equations in the optimization problem which makes it nonlinear and nonconvex, a power flow solver is utilized and the problem is solved by blackbox optimization (BBO). The feasibility of the derived solution is improved by using unbalanced power flow simulations. The solution is effective under various demand and DER generation scenarios such that device settings are not frequently changed, making it practical for in-field implementations. Through numerical simulations on IEEE test feeders, we illustrate the performance of the solutions of our proposed approach on both in-sample and out-of-sample scenarios. We show that our approach outperforms a benchmark reinforcement learning method, and is also scalable to large-scale distribution networks.
2025-12-31
IEEE Transactions on Power Delivery (published)
doi.org
Distributed Combined Space Partitioning and Network Flow Optimization: an Optimal Transport Approach
Théo Laurentin
Patrick Coirault
Emmanuel Moulay
Antoine Lesage-Landry
Jerome Le Ny
2025-12-08
2025 IEEE 64th Conference on Decision and Control (CDC) (published)
doi.org
arxiv.org
A Comprehensive Review of Transmission and Distribution Optimal Power Flow Problems for the Integration of Distributed Energy Resources
Samuel M. Muhindo
Hanane Dagdougui
Antoine Lesage-Landry
Hussein Suprême
This paper presents a comprehensive review of coordination methods for addressing large-scale transmission and distribution optimal power fl… (see more)ow (TDOPF) problems involving distributed energy resources. With distinct objectives, each transmission and distribution system operator (TSO/DSO) independently seeks to solve its own optimal power flow (OPF) instance. First, iterative methods are reviewed, in which the central OPF is solved recursively by decomposing the full problem into smaller, more manageable sub-problems or by replacing peripheral portions of the network within the central OPF with reduced equivalent grids. Generally, the convergence to an optimal solution of the full problem when all sub-OPFs are coordinated is not guaranteed as iterative methods repeat procedures until the changes in control variables of the central OPF are minimal. Second, sequential methods are reviewed, in which the central OPF is solved sequentially in a fixed, nonrepeating procedure by considering previous results. Achieving a fair balance between TSO and DSO interests in sequential methods might adversely affect the performance of a largescale central OPF. The advantages and the limitations of the two coordination methods are presented based on the operation mode of TSO-DSO network. Future research opportunities for coordination methods of TSO-DSO network are drawn using the Kron reduction method and mean-field games.
2025-10-14
2025 IEEE Electrical Power and Energy Conference (EPEC) (published)
doi.org
Joint Satellite Power Consumption and Handover Optimization for LEO Constellations
Yassine Afif
Mohammed Almekhlafi
Antoine Lesage-Landry
Gunes Karabulut Kurt
In satellite constellation-based communication systems, continuous user coverage requires frequent handoffs due to the dynamic topology indu… (see more)ced by the Low Earth Orbit (LEO) satellites. Each handoff between a satellite and ground users introduces additional signaling and power consumption, which can become a significant burden as the size of the constellation continues to increase. This work focuses on the optimization of the total transmission rate in a LEO-to-user system, by jointly considering the total transmitted power, user-satellite associations, and power consumption, the latter being handled through a penalty on handoff events. We consider a system where LEO satellites serve users located in remote areas with no terrestrial connectivity, and formulate the power allocation problem as a mixed-integer concave linear program (MICP) subject to power and association constraints. Our approach can be solved with off-the-shelf solvers and is benchmarked against a naive baseline where users associate to their closest visible satellite. Extensive Monte Carlo simulations demonstrate the effectiveness of the proposed method in controlling the handoff frequency while maintaining high user throughput. These performance gains highlight the effectiveness of our handover-aware optimization strategy, which ensures that user rates improve significantly, by about 40%, without incurring a disproportionate rise in the handoff frequency.
2025-10-13
International Conference on Wireless for Space and Extreme Environments (published)
doi.org
arxiv.org
On the frequency variation in load-flow calculations for islanded alternating current microgrids
Matthias Molénat
Jean Mahseredjian
Nasim Rashidirad
Antoine Lesage-Landry
2025-08-31
Mathematics and Computers in Simulation (published)
doi.org
Distributed Combined Space Partitioning and Network Flow Optimization: an Optimal Transport Approach (Extended Version)
Théo Laurentin
Patrick Coirault
Emmanuel Moulay
Antoine Lesage-Landry
Jerome Le Ny
2025-08-28
ArXiv (preprint)
arxiv.org
arxiv.org
Efficient Deep Reinforcement Learning-Based Supplementary Damping Control with a Coordinated RMS Training and EMT Testing Scheme
Tao Xue
Mingxuan Zhao
Ilhan Kocar
Mohsen Ghafouri
Antoine Lesage-Landry
Siqi Bu
Ziqing Zhu
2025-07-31
IEEE Transactions on Power Delivery (published)
doi.org
Multi-Priority Scheduling for Traffic Management in Future Scalable Payloads
Zineb Garroussi
Olfa Ben Yahia
Brunilde Sansò
Jean-François Frigon
Stéphane Martel
Guillaume Mantelet
Antoine Lesage-Landry
Gunes Karabulut Kurt
Through multibeam, frequency reuse, and advanced antenna technology, regenerative non-geostationary orbit (NGSO) extremely high-throughput s… (see more)atellites (EHTS) are expected to play a key role in future communications, delivering data rates up to terabits per second. This paper investigates a novel architecture for future regenerative and scalable payloads to satisfy users’ demands for varying quality of service (QoS). This architecture is designed based on multiple modem banks and requires a new flow assignment strategy to efficiently route traffic within the satellite. We propose a multi-commodity path flow optimization problem to manage the load with varying QoS requirements across multiple modems within an NGSO high-throughput satellite (HTS) system and beyond. The simulation results demonstrate that the proposed model consistently maintains low delays and packet losses for the highest-priority traffic and outperforms the classical first-in, first-out (FIFO) approach.
2025-06-07
2025 IEEE International Conference on Communications Workshops (ICC Workshops) (published)
doi.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.
2025-04-16
ArXiv (preprint)
doi.org
arxiv.org
Online Interior-point Methods for Time-varying Equality-constrained Optimization
Jean-Luc Lupien
Iman Shames
Antoine Lesage-Landry
2025-03-31
IEEE Transactions on Automatic Control (published)
doi.org
arxiv.org