Philip Payne — accepted AI-conference papers

2026

GALAX: Graph-Augmented Language Model for Explainable Reinforcement-Guided Subgraph Reasoning in Precision Medicine

Heming Zhang, Di Huang, Wenyu Li, Michael A Province, Yixin Chen, Philip Payne +1

ICLR 2026poster

In precision medicine, quantitative multi-omic features, topological context, and textual biological knowledge play vital roles in identifying disease-critical signaling pathways and targets, guiding the discovery of novel therapeutics and effective treatment strategies. Existing pipelines capture o…

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2024

Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability

Zehao Dong, Muhan Zhang, Philip Payne, Michael A Province, Carlos Cruchaga, Tianyu Zhao +2

ICLR 2024poster

The expressivity of Graph Neural Networks (GNNs) has been studied broadly in recent years to reveal the design principles for more powerful GNNs. Graph canonization is known as a typical approach to distinguish non-isomorphic graphs, yet rarely adopted when developing expressive GNNs. This paper pro…

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