Portrait de Emma Frejinger

Emma Frejinger

Membre académique associé
Professeure agrégée, Université de Montréal, Département d'informatique et de recherche opérationnelle

Biographie

Emma Frejinger est professeure agrégée au Département d'informatique et de recherche opérationnelle (DIRO) de l'Université de Montréal, où elle est aussi titulaire d'une chaire de recherche du Canada et d'une chaire industrielle financée par la Compagnie des chemins de fer nationaux du Canada. Ses recherches sont axées sur les applications et se concentrent sur des combinaisons novatrices de méthodologies issues de l'apprentissage automatique et de la recherche opérationnelle pour résoudre des problèmes de prise de décision à grande échelle. Emma Frejinger possède une vaste expérience de travail avec l'industrie, en particulier dans le secteur des transports, où elle a dirigé des projets de recherche collaborative. Depuis 2018, elle est également conseillère scientifique pour IVADO Labs, où elle contribue au développement de solutions d'IA pour l'industrie de la chaîne d'approvisionnement. Avant de se joindre à l'Université de Montréal, en 2013, elle était membre du corps professoral de l'Institut royal de technologie KTH, en Suède. Elle est titulaire d'un doctorat en mathématiques de l'École polytechnique fédérale de Lausanne (EPFL), en Suisse.

Étudiants actuels

Ben Hudson
Doctorat - Université de Montréal
Superviseur⋅e principal⋅e :
Laurent Charlin
ben.hudson@mila.quebec
Site web
Github
Google Scholar

Publications

Assessing the Impact: Does an Improvement to a Revenue Management System Lead to an Improved Revenue?
Greta Laage
Emma Frejinger
Andrea Lodi
Guillaume Rabusseau
2021-01-13
ArXiv (prépublication)
arxiv.org
arxiv.org
Estimating the Impact of an Improvement to a Revenue Management System: An Airline Application
Greta Laage
Emma Frejinger
William Hamilton
Andrea Lodi
Guillaume Rabusseau
Airlines have been making use of highly complex Revenue Management Systems to maximize revenue for decades. Estimating the impact of changin… (voir plus)g one component of those systems on an important outcome such as revenue is crucial, yet very challenging. It is indeed the difference between the generated value and the value that would have been generated keeping business as usual, which is not observable. We provide a comprehensive overview of counterfactual prediction models and use them in an extensive computational study based on data from Air Canada to estimate such impact. We focus on predicting the counterfactual revenue and compare it to the observed revenue subject to the impact. Our microeconomic application and small expected treatment impact stand out from the usual synthetic control applications. We present accurate linear and deep-learning counterfactual prediction models which achieve respectively 1.1% and 1% of error and allow to estimate a simulated effect quite accurately.
2021-01-01
arXiv.org (prépublication)
dblp.uni-trier.de
Electric Vehicles Equilibrium Model that Considers Queue Delay and Mixed Traffic
Nurit Oliker
Miguel F. Anjos
Margarida Carvalho
Emma Frejinger
Bernard Gendron
This study develops an equilibrium model for electric vehicles (EVs) that considers both queue delays in charging stations and flow dependen… (voir plus)t travel times. This is a user equilibrium model that accounts for travel, charging and queuing time in the path choice modelling of EVs and the complementary traffic. Waiting and service times in charging stations are represented by an m/m/k queuing system. The model considers multiple vehicle and driver classes, expressing different battery capacity, initial charge state and range anxiety level. Feasible paths are found for multiple classes given their limited travel range. A numerical application exemplifies the limitations of EVs assignment and their impact on flow distribution.
2020-01-01
(publié)
www.semanticscholar.org
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
Eric P. Larsen
Sébastien Lachapelle
Yoshua Bengio
Emma Frejinger
Simon Lacoste-Julien
Andrea Lodi
This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a method… (voir plus)ology to quickly predict expected tactical descriptions of operational solutions (TDOSs). The problem we address occurs in the context of two-stage stochastic programming, where the second stage is demanding computationally. We aim to predict at a high speed the expected TDOS associated with the second-stage problem, conditionally on the first-stage variables. This may be used in support of the solution to the overall two-stage problem by avoiding the online generation of multiple second-stage scenarios and solutions. We formulate the tactical prediction problem as a stochastic optimal prediction program, whose solution we approximate with supervised machine learning. The training data set consists of a large number of deterministic operational problems generated by controlled probabilistic sampling. The labels are computed based on solutions to these problems (solved independently and offline), employing appropriate aggregation and subselection methods to address uncertainty. Results on our motivating application on load planning for rail transportation show that deep learning models produce accurate predictions in very short computing time (milliseconds or less). The predictive accuracy is close to the lower bounds calculated based on sample average approximation of the stochastic prediction programs.
2018-07-31
ArXiv (preprint)
doi.org
arxiv.org