I'm interested in machine learning and combinatorial optimization, mainly in their application in an industrial context. Particularly the fusion of the two when the output of machine learning algorithms becomes an input for combinatorial optimization models. For example, I've been working on the optimization of an electric vehicle route (combinatorial optimization) that takes machine learning models as input to predict electricity consumption. The optimization models can vary the speed of the vehicle, impacting the consumption predicted by the machine learning models. Another example would be the detection of anomalies in the sound emitted by an industrial planer, then real-time optimization of its parameters to correct the sound. So I have expertise in both machine learning and combinatorial optimization.