Clément's work focuses on the design of neural architectures for structured data: sets, graphs and point clouds. These problems have in common a large symmetry group, which is the invariance to all possible permutations of the points. In order to design architectures that are both computationally and data efficient, these symmetries must be accounted for. This raises several challenges, as existing functions often come with a tradeoff between computational cost and expressive power. During the first years of his PhD, Clément has developed new insights and methods to design such functions. The next steps consist in adapting these methods to the problem of graph generation and especially to molecule generation for drug discovery, which combines both interesting theoretical challenges and a large interest from the industry. This task has not been studied very extensively from a graph perspective yet, and several innovations from graph representation learning could be used to improve existing methods. The University of Amsterdam has a renowned expertise in generative models (with the development of Variational Autoencoders and important contributions to Flow-based methods) and symmetry-aware machine learning. We hope that, combined with our experience with geometric deep learning, it will result in a fruitful collaboration.