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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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Fast Matrix Multiplication Without Tears: A Constraint Programming Approach
29th International Conference on Principles and Practice of Constraint Programming (CP 2023), 2023
A constraint programming approach to reducing the number of multiplications needed for matrix multiplication.
Deza, A., Liu, C., Vaezipoor, P., & Khalil, E. B. (2023). "Fast Matrix Multiplication Without Tears: A Constraint Programming Approach." CP.
Machine Learning for Cutting Planes in Integer Programming: A Survey
International Joint Conference on Artificial Intelligence IJCAI (Survey Track), 2023
A survey of machine learning techniques for selecting cutting planes in integer programming.
Deza, A., & Khalil, E. B. (2023). "Machine Learning for Cutting Planes in Integer Programming: A Survey." IJCAI (Survey Track).
A Motivational Interviewing Chatbot With Generative Reflections for Increasing Readiness to Quit Smoking: Iterative Development Study
The Journal of Medical Internet Research - Mental Health, 2023
An iterative development study of a motivational interviewing chatbot with generative reflections.
Brown, A., Kumar, A. T., Melamed, O., Ahmed, I., Wang, Y. H., Deza, A., et al. (2023). "A Motivational Interviewing Chatbot With Generative Reflections for Increasing Readiness to Quit Smoking: Iterative Development Study." JMIR Mental Health.
Machine Learning for Optimization-Based Separation of Mixed-Integer Rounding Cuts
Under Review, 2024
Machine learning for optimization-based separation of mixed-integer rounding cuts in mixed-integer linear programming.
Guaje, O., Deza, A., Kazachkov, A. M., & Khalil, E. B. (2024). "Machine Learning for Optimization-Based Separation of Mixed-Integer Rounding Cuts." (under review). arXiv:2408.08449.
DualSchool: Robust Quantitative Evaluation of Leading LLMs on Simple OR Tasks?
Under Review, 2025
A quantitative evaluation of leading large language models on simple operations research tasks.
Klamkin, M., Deza, A., Cheng, S., Zhao, H., & Van Hentenryck, P. (2025). "DualSchool: Robust Quantitative Evaluation of Leading LLMs on Simple OR Tasks?" (under review). arXiv:2505.21775.
Learn2Aggregate: Supervised Generation of Chvatal-Gomory Cuts Using Graph Neural Networks
Association for the Advancement of Artificial Intelligence, 2025
A graph neural network approach to supervised generation of Chvatal-Gomory cuts for mixed-integer linear programs.
Deza, A., Khalil, E. B., Fan, Z., Zhou, Z., & Zhang, Y. (2025). "Learn2Aggregate: Supervised Generation of Chvatal-Gomory Cuts Using Graph Neural Networks." AAAI.
Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches
Under Review, 2026
LLM-guided model patches for making large-scale re-optimization more accessible and efficient.
Ye, T., Deza, A., Mohan, V., Er Raqabi, E. M., & Van Hentenryck, P. (2026). "Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches." (under review). arXiv:2605.18692.