معرفی
Rex Ying is an Assistant Professor in the Department of Computer Science at Yale University's School of Engineering & Applied Science. His research focuses on developing expressive, scalable, and explainable algorithms for graph-structured data through graph neural networks and geometric learning.
His work spans graph learning applications in recommender systems, anomaly detection, social network analysis, protein networks, drug discovery, and physical simulations. Key research thrusts include Non-Euclidean Foundation Models, Geometric Deep Learning, and Explainable AI for graph representations, with recent emphasis on hyperbolic geometry for hierarchical data modeling.
Recent publications reveal strong trends in foundation models for scientific discovery (particularly spatial biology), hyperbolic adaptations of LLMs, and multimodal integration for biomedical applications. His lab actively bridges theoretical geometric learning with real-world applications in biology and physics.
- Baidu Scholarship 2019
- Area Chair for LoG 2022 Conference
- Blue Sky Best Paper Award at ACM KDD 2025
- NSF core program award on foundation models for scientific discovery
Ying leads the Graph and Geometric Learning Lab, which develops open-source tools like PyTorch Geometric. He actively recruits PhD students for research on GNN frontiers, theoretical studies of graph learning, and applications in social/natural sciences. Recent grants include NSF funding for scientific foundation models and industry collaborations with Amazon/Ericsson.
The lab maintains strong industry partnerships with telecom (Ericsson), healthcare (spatial omics), and social platforms, while organizing key workshops like Non-Euclidean Foundation Models at WebConf 2025 and Graph Signal Processing at GSP 2025.
