Justin Dumouchelle

Assistant Professor
Department of Mathematics & Statistics, University of Calgary

I am an Assistant Professor at the University of Calgary in the Department of Mathematics & Statistics. My research interests are at the intersection of machine learning and operations research. I am focused on developing efficient data-driven algorithms for complex decision-making problems, particularly in settings involving discrete decisions, uncertainty, and multiple decision-makers, with applications in logistics, transportation, and healthcare.

Prospective Graduate Students: I am recruiting Master's and Ph.D. students to begin in Fall 2026. I welcome applicants from Operations Research, Computer Science, Applied Mathematics, Industrial Engineering, Statistics, and other quantitative fields aligned with optimization and machine learning. If you are interested, please email me your CV and transcripts; however, due to volume, I may not be able to reply to all inquiries.
UCalgary Undergraduate Students: I also welcome motivated undergraduate students from the University of Calgary who are interested in gaining research experience in machine learning and operations research to reach out about Undergraduate Research Summer Studentship opportunities.

I completed my Ph.D. in Mechanical and Industrial Engineering Department at the University of Toronto under the supervision of Elias Khalil. Prior to my Ph.D., I completed my MASc in Applied Mathematics under the supervision of Andrea Lodi and Emma Frejinger at Polytechnique Montréal and my BMath in Computer Science and Combinatorics & Optimization from the University of Waterloo. I worked in industry for one year as a Machine Learning Research Intern at Borealis AI on applied machine learning projects.

Research Areas: Operations Research, Integer Programming, Stochastic Programming, Robust Optimization, Optimization Under Uncertainty, Machine Learning

Publications

Working Papers

Deep Learning for Two-Stage Robust Integer Optimization
J. Dumouchelle, E. Julien, J. Kurtz, and E. B. Khalil
Major Revision in Operations Research, 2025

Conference Papers

Neur2BiLO: Neural Bilevel Optimization
J. Dumouchelle, E. Julien, J. Kurtz, and E. B. Khalil
Advances in Neural Information Processing Systems (NeurIPS), 2024
Neur2RO: Neural Two-Stage Robust Optimization
J. Dumouchelle, E. Julien, J. Kurtz, and E. B. Khalil
International Conference on Learning Representations (ICLR), 2024
Neur2SP: Neural Two-Stage Stochastic Programming
J. Dumouchelle*, R. Patel*, E. B. Khalil, and M. Bodur
Advances in Neural Information Processing Systems (NeurIPS), 2022
The machine learning for combinatorial optimization competition (ML4CO): Results and insights
M. Gasse, S. Bowly, Q. Cappart, J. Charfreitag, L. Charlin, D. Chételat, A. Chmiela, J. Dumouchelle, et al.
Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, PMLR, 2022

Journal Papers

Reinforcement Learning for Freight Booking Control Problems
J. Dumouchelle, E. Frejinger, and A. Lodi
Journal of Revenue and Pricing Management, 2024

Workshop Papers

Ecole: A gym-like library for machine learning in combinatorial optimization solvers
A. Prouvost, J. Dumouchelle, L. Scavuzzo, M. Gasse, D. Chételat, and A. Lodi
Learning Meets Combinatorial Algorithms at NeurIPS 2020, 2020

* denotes equal contribution.

Teaching

University of Calgary

Course Instructor
  • DATA 607 - Statistical and Machine Learning, Winter 2026
  • DATA 543 - Deep Learning, Winter 2026

University of Toronto

Course Instructor
  • MIE245 - Data Structures and Algorithms, Winter 2025
Teaching Assistant
  • MIE245 - Data Structures and Algorithms, Tutorial TA, Winter 2024
  • MIE335 - Algorithms and Numerical Methods, Tutorial TA, Winter 2023