Portrait of Emma Frejinger

Emma Frejinger

Associate Academic Member
Associate Professor, Université de Montréal, Department of Computer Science and Operations Research Department
Research Topics
AI and Sustainability
Applied Machine Learning
Combinatorial Optimization
Optimization
Reinforcement Learning
Website

Biography

Emma Frejinger is an associate professor in the Department of Computer Science and Operations Research at Université de Montréal (UdeM). She holds a Canada Research Chair and an industrial chair funded by the Canadian National Railway Company.

Her research is application-driven and focuses on using innovative combinations of methodologies from machine learning and operations research to solve large-scale decision-making problems. She has extensive experience working with industry, particularly within the transportation sector, where she has led collaborative research projects.

Since 2018, Frejinger has also worked as a scientific advisor for IVADO Labs, developing AI solutions for the supply chain industry. Before joining UdeM in 2013, she was on the faculty of the KTH Royal Institute of Technology in Sweden. She holds a PhD in mathematics from the Swiss Federal Institute of Technology (EPFL).

Current Students

Ben Hudson
PhD - Université de Montréal
Principal supervisor :
Laurent Charlin
ben.hudson@mila.quebec
Website
Github
Google Scholar

Publications

Estimating the Impact of an Improvement to a Revenue Management System: An Airline Application
Greta Laage
Emma Frejinger
William L. 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… (see more)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 (preprint)
dblp.uni-trier.de
A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs
Yoshua Bengio
Emma Frejinger
Andrea Lodi
Rahul Anuj Patel
Sriram Sankaranarayanan
2020-09-19
Integration of Constraint Programming, Artificial Intelligence, and Operations Research (published)
doi.org
arxiv.org
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… (see more)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.
Predicting Solution Summaries to Integer Linear Programs under Imperfect Information with Machine Learning
Eric Larsen
Sébastien Lachapelle
Yoshua Bengio
Emma Frejinger
Simon Lacoste-Julien
Andrea Lodi
The paper provides a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a metho… (see more)dology to quickly predict solution summaries (i.e., solution descriptions at a given level of detail) to discrete stochastic optimization problems. We approximate the solutions based on supervised learning and the training dataset consists of a large number of deterministic problems that have been solved independently and offline. Uncertainty regarding a missing subset of the inputs is addressed through sampling and aggregation methods. Our motivating application concerns booking decisions of intermodal containers on double-stack trains. Under perfect information, this is the so-called load planning problem and it can be formulated by means of integer linear programming. However, the formulation cannot be used for the application at hand because of the restricted computational budget and unknown container weights. The results show that standard deep learning algorithms allow one to predict descriptions of solutions with high accuracy in very short time (milliseconds or less).
2018-07-31
arXiv.org (preprint)
dblp.uni-trier.de
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… (see more)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