Yujun Yan
Assistant Professor
Yujun's research lies at the intersection of machine learning and network science, with a focus on modeling complex topological and relational structures. Her work aims to develop expressive and generalizable graph-based machine learning models by integrating theoretical insights with practical design principles. Beyond foundational advances, she also explores structural modeling in domains such as neuroscience, knowledge reasoning, and multi-agent systems.
Contact
Department(s)
Computer Science
Education
- BS Southeast University, Nanjing China
- MS University of Michigan-Ann Arbor
- PhD University of Michigan-Ann Arbor
Selected Publications
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra. The IEEE International Conference on Data Mining (ICDM) 2022.
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra. Conference on Neural Information Processing Systems (NeurIPS) 2020.
Neural Execution Engines: Learning to Execute Subroutines. Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi. Conference on Neural Information Processing Systems (NeurIPS) 2020.
Exploring Consistency in Graph Representations: From Graph Kernels to Graph Neural Networks. Xuyuan Liu, Yinghao Cai, Qihui Yang, Yujun Yan. Conference on Neural Information Processing Systems (NeurIPS) 2024.
Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning. Zheng Huang, Qihui Yang, Dawei Zhou, Yujun Yan. International Conference on Machine Learning (ICML) 2024.