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 focuses on developing algorithms combining AI and optimization to address complex combinatorial and mixed-integer optimization problems involving nested or sequential decisions, with applications in energy systems and logistics.

I completed my Ph.D. at the University of Toronto under the supervision of Elias Khalil, my MASc at Polytechnique Montréal with Andrea Lodi and Emma Frejinger, and my BMath at the University of Waterloo. I also spent one year working at Borealis AI.

Research Areas: Operations Research, Integer Programming, Stochastic Programming, Robust Optimization, Optimization Under Uncertainty, Machine Learning
Prospective Students
  • Graduate Students (MSc/PhD): I am recruiting students to begin in Fall 2027 and later. I welcome applicants from Operations Research, Computer Science, Applied Mathematics, Industrial Engineering, Statistics, and related quantitative fields aligned with optimization and machine learning. If interested, complete the Graduate Student Interest Form, rather than emailing me. See my supervisor profile for details, admission requirements, and what to expect.
  • Undergraduate Research: Students interested in summer research opportunities are welcome to reach out. Please email with the subject "Summer Undergraduate Research" and include a brief description of your interests, along with a resume/CV, and transcripts.

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
Paper Website

Conference Papers

Neur2BiLO: Neural Bilevel Optimization
J. Dumouchelle, E. Julien, J. Kurtz, and E. B. Khalil
Advances in Neural Information Processing Systems (NeurIPS), 2024
Paper Website Code
Neur2RO: Neural Two-Stage Robust Optimization
J. Dumouchelle, E. Julien, J. Kurtz, and E. B. Khalil
International Conference on Learning Representations (ICLR), 2024
Paper Website Code Talk
Neur2SP: Neural Two-Stage Stochastic Programming
J. Dumouchelle*, R. Patel*, E. B. Khalil, and M. Bodur
Advances in Neural Information Processing Systems (NeurIPS), 2022
Paper Website Code
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
Paper Website

Journal Papers

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

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
Paper Website Code

* 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

Outside research and teaching, I enjoy spending time with my dog, hiking, cooking, and specialty coffee.