Zhitao Ying, Ph.D., Stanford University; B.S., Duke University. Joined Yale Faculty July 2022.

See Google Scholar profile for the entire list of publications.

• Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones, NeurIPS 2021
• Neural Distance Embeddings for Biological Sequences, NeurIPS 2021
• Bipartite Dynamic Representations for Abuse Detection, KDD 2021
• Identity-aware Graph Neural Networks, AAAI 2021
• (Ph.D. Thesis) Towards Expressive and Scalable Deep Representation Learning for Graphs, 2021
• Multi-hop Attention Graph Neural Network, IJCAI 2021
• Learning to Simulate Complex Physics with Graph Networks, ICML 2020
• Redundancy-free Computation for Graph Neural Networks, KDD 2020
• Design space for graph neural networks, NeurIPS 2020
• Neural Execution of Graph Algorithms, ICLR 2020
• Gnnexplainer: Generating Explanations for Graph Neural Networks, NeurIPS 2019
• Hyperbolic Graph Convolutional Neural Networks, NeurIPS 2019
• Position-aware Graph Neural Networks, ICML 2019
• Graph Convolutional Neural Networks for Web-scale Recommender Systems, KDD 2018
• Hierarchical Graph Representation Learning with Differentiable Pooling, NeurIPS 2018
• Graph Convolutional Policy Network for Goal-directed Molecular Graph Generation, NeurIPS 2018
• Graphrnn: Generating Realistic Graphs with Deep Auto-regressive Models, ICML 2018
• Inductive Representation Learning on Large Graphs, NeurIPS 2017
• Representation learning on graphs: Methods and applications, IEEE Data Engineering Bulletin 2017