Identify universal building blocks for robust and scalable GNNs.
Representation learning for drawings via graphs with geometric and temporal information.
Scalable deep learning systems for practical NP-Hard combinatorial problems such as the TSP.
Chemical synthesis, structure and property prediction using deep neural networks.
Graph Neural Network architectures for inductive representation learning on arbitrary graphs.