Jiong Zhu is a Ph.D. candidate at University of Michigan. His research focuses on graph representation learning and graph neural networks, especially on their limitations, robustness, fairness and real-world applications under complex and large-scale environments. His research has been published in major AI and Data Mining conferences, including NeurIPS, AAAI, and KDD. He also served as reviewers for AI and Data Mining journals and conferences, and organizers of Workshop on Graph Learning Benchmark (GLB).