Publications

You can also find my articles on my Google Scholar. My research can be summarised by the following keywords:

• Methodologies-Oriented: Unsupervised Machine Learning; Anomaly Detection; Transfer Learning; XAI; Graph Neural Networks
• Applications-Oriented: Log Analysis; Predictive Maintenance; Digital Twin; AI for Manufacturing

2024

• Automated Feature Selection for Anomaly Detection.
  • Zhong Li, Yuhang Wang, Milan Luijken, Samuel Smulders, Matthijs van Leeuwen. In Preparation
• Adversarial Attacks on Graph Explanations.
  • Zhong Li, Simon Geisler, Yuhang Wang, Stephan Günnemann, Matthijs van Leeuwen. Under Review for a ML Conference .
• Automated Self-Supervised Learning for Graph Anomaly Detection.
  • Zhong Li, Yuhang Wang, Matthijs van Leeuwen. Under Review for DAMI.
• Cross-Domain Graph Level Anomaly Detection.
  • Zhong Li, Sheng Liang, Jiayang Shi, Matthijs van Leeuwen. Under Review for TKDE.
• Graph Neural Networks based Log Anomaly Detection and Explanation.
  • Zhong Li, Jiayang Shi, Matthijs van Leeuwen. ICSE’24 poster track.
  • [paper], [code] (ArXiv full version)
  • [paper] (ICSE short version)
  • the full version has been submitted to the EAAI journal, and we are testing this algorithm on ASML’s lithography machines

2023

• A Survey on Explainable Anomaly Detection.
  • Zhong Li, Yuxuan Zhu, Matthijs van Leeuwen. ACM Transactions on Knowledge Discovery from Data (TKDD). 23: 1–54.
  • [paper]
• Explainable Contextual Anomaly Detection using Quantile Regression Forests.
  • Zhong Li, Matthijs van Leeuwen. Data Mining and Knowledge Discovery (DAMI). 37: 2517–2563.
  • [paper], [code]

2022

• Feature Selection for Fault Detection and Prediction based on Log Analysis.
  • Zhong Li, Matthijs van Leeuwen. SIGKDD Explorations Newsletter. 24(2): 96–104.
  • [paper]
• Choosing and changing the analysis scale in non-inferiority trials with a binary outcome.
  • Zhong Li, Matteo Quartagno, Stefan Böhringer, and Nan van Geloven. Clinical Trials. 19(1): 14-21.
  • [paper]