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1/

Network modeling and
analysis

To understand and characterize dynamical interactions underlying complex processes including causal interactions for various applications including predictive medicine, social organization and collective decision making, game theory, influence spreading and source of bias in networks, topological properties of networks, connectome analysis, and others!

2/

Explainable machine learning

Enabling neural networks to understand concepts. We are interested in developing new methods and models for generalized and scalable intelligence by integrating domain knowledge about the system. We are interested in applications such as topological AI for drug discovery and material development, physics-informed ML for climate prediction and modeling fluid structure, theoretical analysis of graph neural networks, and brain-inspired ML.