Portrait of Margarida Carvalho

Margarida Carvalho

Associate Academic Member
Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
AI and Sustainability
Algorithmic Fairness
Combinatorial Optimization
Decision Theory
Game Theory
Optimization
Website
Google Scholar

Biography

Margarida Carvalho holds a bachelor’s and master’s degree in mathematics. She completed her PhD in computer science at the University of Porto, for which she received the 2018 EURO Doctoral Dissertation Award.

In 2018, Carvalho was appointed assistant professor in the Department of Computer Science and Operations Research at Université de Montréal, where she holds the FRQ-IVADO Research Chair in Data Science for Combinatorial Game Theory.

She is an expert in operations research, in particular, combinatorial optimization and algorithmic game theory. Her research is motivated by real-world decision-making problems that involve the interaction of multiple agents, such as kidney exchange programs, school choice and competitive markets.

Current Students

Marylou Fauchard
Master's Research - Université de Montréal
Co-supervisor :
Golnoosh Farnadi
William St-Arnaud
PhD - Université de Montréal
Principal supervisor :
Golnoosh Farnadi

Publications

Reasoning with Preference Constraints: A Benchmark for Language Models in Many-to-One Matching Markets
Marylou Fauchard
Florian Carichon
Margarida Carvalho
Golnoosh Farnadi
2025-09-22
NeurIPS.cc/2025/Workshop/WiML (published)
openreview.net
openreview.net
Adaptation, Comparison and Practical Implementation of Fairness Schemes in Kidney Exchange Programs
William St-Arnaud
Margarida Carvalho
Golnoosh Farnadi
In Kidney Exchange Programs (KEPs), each participating patient is registered together with an incompatible donor. Donors without an incompat… (see more)ible patient can also register. Then, KEPs typically maximize overall patient benefit through donor exchanges. This aggregation of benefits calls into question potential individual patient disparities in terms of access to transplantation in KEPs. Considering solely this utilitarian objective may become an issue in the case where multiple exchange plans are optimal or near-optimal. In fact, current KEP policies are all-or-nothing, meaning that only one exchange plan is determined. Each patient is either selected or not as part of that unique solution. In this work, we seek instead to find a policy that contemplates the probability of patients of being in a solution. To guide the determination of our policy, we adapt popular fairness schemes to KEPs to balance the usual approach of maximizing the utilitarian objective. Different combinations of fairness and utilitarian objectives are modelled as conic programs with an exponential number of variables. We propose a column generation approach to solve them effectively in practice. Finally, we make an extensive comparison of the different schemes in terms of the balance of utility and fairness score, and validate the scalability of our methodology for benchmark instances from the literature.
2025-08-01
European Journal of Operational Research (published)
doi.org
arxiv.org
Computing Approximate Nash Equilibria for Integer Programming Games
Aloïs Duguet
Margarida Carvalho
Gabriele Dragotto
Sandra-ulrich Ngueveu
2025-07-31
Optimization Letters (published)
doi.org
arxiv.org
Solving Combinatorial Pricing Problems using Embedded Dynamic Programming Models
Quang Minh Bui
Margarida Carvalho
José Neto
The combinatorial pricing problem (CPP) is a bilevel problem in which the leader maximizes their revenue by imposing tolls on certain items … (see more)that they can control. Based on the tolls set by the leader, the follower selects a subset of items corresponding to an optimal solution of a combinatorial optimization problem. To accomplish the leader's goal, the tolls need to be sufficiently low to discourage the follower from choosing the items offered by the competitors. In this paper, we derive a single-level reformulation for the CPP by rewriting the follower's problem as a longest path problem using a dynamic programming model, and then taking its dual and applying strong duality. We proceed to solve the reformulation in a dynamic fashion with a cutting plane method. We apply this methodology to 2 distinct dynamic programming models, namely, a novel formulation designated as selection diagram and the well-known decision diagram. We also produce numerical results to evaluate their performances across 3 different specializations of the CPP and a closely related problem that is the knapsack interdiction problem. Our results showcase the potential of the 2 proposed reformulations over the natural value function approach, expanding the set of tools to solve combinatorial bilevel programs.
2025-04-28
INFORMS Journal on Computing (published)
doi.org
arxiv.org
What makes a good public EV charging station? A revealed preference study
Steven Lamontagne
Margarida Carvalho
Emma Frejinger
Ribal Atallah
2025-04-24
ArXiv (preprint)
arxiv.org
arxiv.org
What makes a good public EV charging station? A revealed preference study
Steven Lamontagne
Margarida Carvalho
Emma Frejinger
Ribal Atallah
To determine the optimal locations for electric vehicle charging stations, optimisation models need to predict which charging stations users… (see more) will select. We estimate discrete choice models to predict the usage of charging stations using only readily available information for charging network operators. Our parameter values are estimated from a unique, revealed preferences dataset of charging sessions in Montreal, Quebec. We find that user distance to stations, proximity to home areas, and the number of outlets at each station are significant factors for predicting station usage. Additionally, amenities near charging stations have a neutral effect overall, with some users demonstrating strong preference or aversion for these locations. High variability among the preferences of users highlight the importance of models which incorporate panel effects. Moreover, integrating mixed logit models within the optimization of charging station network design yields high-quality solutions, even when evaluated under other model specifications.
2025-04-24
ArXiv (preprint)
arxiv.org
arxiv.org
Integer Programming Games.
Margarida Carvalho
Gabriele Dragotto
Andrea Lodi 0001
Sriram Sankaranarayanan 0002
2025-01-01
Found. Trends Optim. (published)
doi.org
Integer Programming Games.
Margarida Carvalho
Gabriele Dragotto
Andrea Lodi
Sriram Sankaranarayanan 0002
2025-01-01
Foundations and Trends® in Optimization (published)
doi.org
A Learning-Based Framework for Fair and Scalable Solution Generation in Kidney Exchange Problems
William St-Arnaud
Margarida Carvalho
Golnoosh Farnadi
2025-01-01
Trans. Mach. Learn. Res. (published)
openreview.net
openreview.net
A stochastic integer programming approach to reserve staff scheduling with preferences
Carl Perreault‐Lafleur
Margarida Carvalho
Guy Desaulniers
2025-01-01
Int. Trans. Oper. Res. (published)
doi.org
arxiv.org
The Strength of Flow Refueling Location Problem Formulations and an Extension to Cyclic Routing
Nagisa Sugishita
Margarida Carvalho
Ribal Atallah
The Flow Refueling Location Problem (FRLP) is a stylized model for determining the optimal placement of refueling stations for vehicles with… (see more) limited travel ranges, such as hydrogen fuel cell vehicles and electric vehicles. A notable extension, the deviation FRLP, accounts for the possibility that drivers may deviate from their preferred routes to refuel or recharge. While solution techniques based on various mathematical programming formulations have been thoroughly explored for this extension, there is a lack of theoretical insights into the relationships and strengths of these formulations. In this work, for the deviation extension, we study two prominent FRLP formulations from the literature and compare their strengths in terms of linear programming (LP) relaxations. We show that the LP relaxation of one formulation yields a bound that is at least as tight as that of the other, which may explain its observed superior performance. Building on these insights, we address a common modeling assumption in the FRLP that requires drivers to use the same paths for their outbound and inbound trips. Specifically, we relax this assumption and introduce the cyclic FRLP, where drivers may use different paths in each direction. We show how existing formulations can be naturally extended to accommodate this setting and describe a branch-and-cut algorithm to solve the problem. We provide numerical experiments highlighting the benefits of such asymmetric routing. For example, in an instance based on the Californian network, the cyclic FRLP serves all demands using 30% fewer facilities than the original FRLP.
2024-09-06
ArXiv (preprint)
arxiv.org
arxiv.org
The Strength of Fuel Refueling Location Problem Formulations
Nagisa Sugishita
Margarida Carvalho
Ribal Atallah
2024-09-06
ArXiv (preprint)
arxiv.org
arxiv.org