My research interests broadly encompass graph representation learning and geometric deep learning. I am fascinated by questions such as

How important is the graph structure for Graph Neural Networks?

  • How critical is the input graph for the downstream task? Can we determine a priori if the input graph contains sufficient information to solve the downstream task? The input graph plays a dual role: it provides the data for the GNN model and also serves as the computational structure upon which message-passing happens. This duality brings up another question: How do we know if we have an optimal computational structure for the learning task? Is the graph structure always relevant?

Generalization for Graph Neural Networks.

  • What does memorization mean in the context of GNNs? Do GNNs even memorize? How can we distinguish between memorization and graph structure overfitting?

Applications to biomedical/modeling molecular data.

  • Designing deep neural networks for biomedical applications such as modeling protein structures, chemical molecules, accelerated drug-target interaction prediction etc.

Talks