I am finishing my PhD within the tau team under the direction of Sylvain chevallier and Guillaume Charpiat, and during which I have worked on the expressivity of neural networks. In particular, I have proposed and implemented
a Neural Architecture Search strategy which jointly optimizes a network architecture and its weights using a new metric named the Expressivity Bottleneck. This metric associates a location of a network architecture to its lack of expressivity by quantifying the ability of the network to follow its functional gradient.
Through my academic courses, I have been studying statistics and classical machine learning tools such as Linear Regression, Random Forest, SVM, and their constrained variants. With my PhD, I changed my object of study and took an interest in neural networks and the understanding of their behaviors when solving one problem or another.
I will start a post-doctoral position in december in the Ockham team.