Explanatory Data Analysis group

Zhong Li

Zhong Li
Zhong Li
PhD candidate

PhD candidate LinkedIn profile

Zhong is currently a PhD student in Machine Learning at LIACS (Leiden Institute of Advanced Computer Science) under the supervision of dr. Matthijs van Leeuwen. Zhong's research interests mainly focus on machine learning, especially in anomaly detection. More specifically, his PhD program centers on the topic of "Feature and data subset selection for contextual anomaly detection using hybrid models", which is part of the DIGITAL TWIN programme funded by NWO.

Before joining Leiden University, Zhong obtained a bachelor's degree in Statistics from Tongji University in Shanghai. Later, he received a Master's degree in Mathematics from Tongji University in Shanghai and a double degree(Diplôme d'Ingénieur) in Data Science from ENSAI in Rennes, France.

In addition to theoretical innovation, Zhong also hopes to successfully apply his research to industries, such as the preventive maintenance of high-end machines, trading compliance in banks, and healthcare in hospitals, etc.

Zhong has always followed the principle of "Tao follows nature". In addition to his interest in mathematics and computer science, he is also passionate about biology, physics, and philosophy. This is because he always believes that things in the universe are interconnected by some kind of universe law, and he likes to get inspiration from various natural and social phenomena.

Selected recent publications

In press
Li, Z, Liang, , Shi, J & van Leeuwen, M Cross-Domain Graph Level Anomaly Detection. Transactions on Knowledge and Data Engineering, ACM
2024
Li, Z, Zhu, Y & van Leeuwen, M A Survey on Explainable Anomaly Detection. Transactions on Knowledge Discovery from Data vol.18(1), ACM, 2024.website
Li, Z, Shi, J & van Leeuwen, M Graph Neural Networks based Log Anomaly Detection and Explanation. In: Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings, pp 306-307, ACM, 2024.
2023
Li, Z & an Leeuwen, M Explainable Contextual Anomaly Detection using Quantile Regression Forests. Data Mining and Knowledge Discovery, Springerwebsite