Bio

I am an Applied Scientist at Amazon GuardDuty. I obtained my Ph.D. in Computer Science at University of Illinois Chicago, advised by Prof. Philip S. Yu. I received my bachelor degree from Beijing University of Posts and Telecommunications and Queen Mary University of London in 2021. I also completed multiple internships as a scientist and engineer at Amazon and Walmart. My research interests include Graph Mining, Anomaly Detection, and Generative Models. More information about me can be found in my Curriculum Vitae.

Selected Publications

• TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scale.
  Kay Liu, Jiahao Ding, MohamadAli Torkamani, Philip S. Yu.
  PAKDD. 2025.
  [Paper][Code]

• Data Augmentation for Supervised Graph Outlier Detection with Latent Diffusion Models.
  Kay Liu, Hengrui Zhang, Ziqing Hu, Fangxin Wang, Philip S. Yu.
  LoG. 2024.
  [Paper][Code]
• BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs.
  Kay Liu, Yingtong Dou, Yue Zhao et al.
  NeurIPS. 2022.
  [Paper][Code][Data][Slides]
• PyGOD: A Python Library for Graph Outlier Detection.
  Kay Liu, Yingtong Dou, Yue Zhao et al.
  JMLR. 2024.
  [Paper][Code]
• Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement.
  Wenjing Chang, Kay Liu, Philip S. Yu, Jianjun Yu.
  TMLR. 2025.
  [Paper][Code]
• Multitask Active Learning for Graph Anomaly Detection.
  Wenjing Chang, Kay Liu, Kaize Ding, Philip S. Yu, Jianjun Yu.
  arXiv preprint. 2024.
  [Paper][Code]

(All publications)