Rate My Professor Pierre Le Bodic

Pierre Le Bodic is a Senior Lecturer in the Department of Data Science and Artificial Intelligence within the Faculty of Information Technology at Monash University. His primary research interests lie in discrete optimisation, focusing on combinatorial optimisation, approximation algorithms, and computational complexity. Le Bodic earned his Doctor of Philosophy in Computer Science from Université Paris-Sud (Paris 11) in 2012. Prior to joining Monash, he was a postdoctoral researcher in the School of Industrial and Systems Engineering at the Georgia Institute of Technology from 2012 to 2016, where he worked under the supervision of Professor George Nemhauser. He commenced his position at Monash University in 2016 as a Lecturer within the optimisation team and advanced to Senior Lecturer.

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.