Rex (Zhitao) Ying

Interests:

For latest news about my lab, visit my lab website: Graph and Geometric Learning Lab

Real-world data can often be expressed in the ubiquitous language of graph, a general concept of entities connected by relations. I focus on developing expressive, scalable and explainable algorithms that incorporate and leverage such relational inductive bias for data expressed as graph structure, via graph neural networks and geometric learning.

I have worked on graph learning applications in recommender system, anomaly detection, social network analysis, protein networks, drug discovery, physical simulations and many more.

Prospective students:

I'm looking for Ph.D. students who are passionate about pushing the frontiers of GNNs and geometric deep learning, through novel modeling of real-world problems and data, theoretical study of GNN and manifolds, new capabilities for graphs (e.g. autoML, explainability and fairness). Students interested in intersections of graph learning and applications in social sciences, natural sciences, medicine and industrial AI platforms are also welcome to reach out.