Rate My Professor Pierre Le Bodic

Le Bodic's work centers on developing algorithms to enhance the performance of mixed-integer programming solvers for general mixed-integer linear programming instances, with additional interests in machine learning, multi-agent path finding, energy systems, and transportation optimization. Key publications include "Learning to branch in mixed integer programming" (AAAI 2016, 596 citations), which introduced learning-based branching strategies; "The SCIP optimization suite 7.0" (2020, 329 citations), contributing to a major open-source solver; "Branch-and-cut-and-price for multi-agent path finding" (2022, 202 citations); "An Abstract Model for Branching and its Application to Mixed Integer Programming" (Mathematical Programming, 2017, 45 citations); "Gradient Boosting Versus Mixed Integer Programming for Sparse Additive Modeling" (2026); and "Degradation-Aware Charger Deployment, Battery Sizing, and Charge Scheduling for Electric Bus Fleets" (2026). His research contributes to UN Sustainable Development Goals such as good health and well-being and zero hunger. Le Bodic supervises student projects in optimisation and is recognized for his innovative teaching philosophy that promotes authentic student engagement, error-normalization in lectures, and the strength of diverse teaching teams.