"On the Interface Between Machine Learning and Discrete Optimization"

In this talk, I will present my personal experience on the interplay between Machine Learning (ML) and Discrete Optimization by discussing three fruitful interactions. Moving from methodological research to applications, the first use case concerns ML-based branching for Mixed-Integer Linear Programming solvers, the second addresses stochastic prediction for tactical planning (applied to railways operations) and the third involves, at the real applied level, demand prediction and seat segmentation in the airlines industry.