Bio

I am a 3rd year Computer Science Ph.D. candidate in Big Data and Social Computing (BDSC) Lab at University of Illinois Chicago. My advisor is Prof. Philip S. Yu. Before joining UIC, I received my bachelor degree from Beijing University of Posts and Telecommunications and Queen Mary University of London in 2021. I am now an applied scientist intern at Amazon based in Seattle. I also interned in Walmart Global Tech and AWS Shanghai AI Lab DGL team. My research interests are Graph Mining, Anomaly Detection, and Fraud Detection. More information about me can be found in my Curriculum Vitae.

Publication

• 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.
  arXiv preprint. 2022.
  [Paper][Code]

• Equal Opportunity of Coverage in Fair Regression.
  Fangxin Wang, Lu Cheng, Ruocheng Guo, Kay Liu, Philip S. Yu.
  NeurIPS. 2023.

• Network Schema Preserving Heterogeneous Information Network Embedding.
  Jianan Zhao, Xiao Wang, Chuan Shi, Zekuan Liu, Yanfang Ye.
  IJCAI. 2020.
  [Paper][Code]

Invited Talk

• Graph Neural Network based Fraud Detection: from Research to Application at Wells Fargo
• Graph Neural Network based Anomaly Detection: from Research to Application at BUAA
• Graph Neural Network based Anomaly Detection: from Research to Application at Novartis
• Leveraging GNNs for Financial Fraud Detection: Practices and Challenges at KDD 2022

Code Contribution

• PyGOD: a Python Library for Graph Outlier Detection (Anomaly Detection)
• Deep Graph Library: a Python Package for Deep Learning on Graphs
• DGFraud-TF2: a Deep Graph-based Toolbox for Fraud Detection in TensorFlow 2.0