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.

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
Prospective Students
  • Graduate Students (MSc/PhD): I am recruiting students to begin in Fall 2026 (and future application cycles). I welcome applicants from Operations Research, Computer Science, Applied Mathematics, Industrial Engineering, Statistics, and related quantitative fields aligned with optimization and machine learning. Please email your resume/CV and transcripts if interested. Due to the volume of inquiries, I may not be able to respond to all messages individually.
  • Undergraduate Research: I am not currently accepting applications for Summer 2026. Students interested in future opportunities are welcome to email a brief description of their 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.