My research interests lie in the fields of machine learning and data mining. Specifically on developing learning algorithms and principles for representation learning on graph structured data.
My recent work can be broadly categorized into the following themes:
- Analysis of graph neural networks with a network science lens.
- Principles for self-supervised graph contrastive learning.
- Temporal graph representation learning with applications to real-world tasks.
My current focus is on various aspects of machine learning for graph data viz. domain adaptation/generalization, invariant learning, explainability and adversarial robustness.
In the past, I have also worked on developing hybrid knowledge graph mining methods and one of my long standing research goals is to unify neural based inductive learning with deductive symbolic methods.
Apart from research, I am into badminton and long-distance running.
Adversarial Graph Augmentation to Improve Graph Contrastive LearningIn Thirty-fourth Conference on Neural Information Processing Systems 2021
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing PatternsIn Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining 2021