I am a Ph.D. candidate in Computer Science at Case Western Reserve University, advised by Prof. Yinghui Wu.

My research focuses on graph machine learning, explainable AI (XAI), and temporal graph neural networks. I am particularly interested in developing counterfactual and interpretable explanation methods for dynamic graph models.

My recent work includes TemGX, a training-free counterfactual explanation framework for temporal graph models, accepted at ICLR 2026. I have also worked on multimodal learning systems that integrate image segmentation, knowledge graphs, and graph neural networks for scientific applications.

In addition, I have developed large-scale benchmarking systems for materials image segmentation, achieving strong performance across SEM, AFM, and XCT datasets, and building pipelines for model evaluation and interpretability.