Foundations of Data Science Seminar Series

Speaker: Rex Ying
Assistant Professor of Computer Science

“Graph Representation Learning: A Geometric Perspective”

Abstract: The talk focuses on geometric embedding approaches to representation learning on graph-structured data. We observe that certain inductive biases of graph data, such as hierarchies and transitive closures, can be modeled more effectively through different embedding geometries. We leverage hyperbolic embeddings, cone embeddings and order embeddings for incorporating these inductive biases of input graph data when learning node and graph representations for large-scale, heterogeneous graph data and challenging tasks.

Speaker Bio:  Rex Ying is an assistant professor in the Department of Computer Science at Yale University. His research focus includes algorithms for graph neural networks, geometric embeddings, and trustworthy ML on graphs. He is the author of many widely used GNN algorithms such as GraphSAGE, PinSAGE and GNNExplainer. In addition, Rex worked on a variety of applications of graph learning in physical simulations, social networks, NLP, knowledge graphs and biology. He developed the first billion-scale graph embedding services at Pinterest, and the graph-based anomaly detection algorithm at Amazon. He is the winner of the dissertation award at KDD 2022.

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