Welcome to my webpage!

Research Interests

My main research interests concern combinatorial optimization and interpretable machine learning, with applications to logistics and supply chain management, production management, resource allocation and information processing.

NEW: We are currently recruiting in the SCALE-AI Chair team. See the announcement HERE. The objective of the SCALE-AI chair is to explore the synergies between operations research (OR) and machine learning (ML) to progress towards a new generation of algorithms for the management of supply chains with essential additional qualities, such as simplicity, transparency, and robustness. Research subjects are open on different subjects, including, among others:
1) Explanation methods for data-driven decision-support algorithms
2) Optimization algorithms for decision support, considering ill-defined constraints arising from practice and extracted from historical solutions, as well as side objectives (simplicity and ease of operationalization of the resulting solutions)
3) Hybrid approaches based on combinatorial optimization and machine learning for stochastic and strategic optimization
4) Combinatorial optimization algorithms for learning-models compression, as well as for certain task of formal verification (for data privacy and fairness)

Resources and Contact

I strive to make all my academic research results accessible, and publish most source codes on my Github profile. Recent research news, open-source codes and articles are also announced on my Twitter feed, visible on the right side of this page.

On this website, you will find other resources: conference presentations, a list of my published works, along with numerous data sets and results for vehicle routing and other challenging problems. Finally, freel free to contact me. I am more than pleased to discuss recent results, case studies, as well as academic or consulting projects.

Contact : thibaut.vidal AT cirrelt.ca