My name is Zhong Li, and I am a final-year Ph.D. candidate in Computer Science at Leiden University, affiliated with the EDA Lab. I am fortunate to be supervised by Dr. Matthijs van Leeuwen (daily supervisor, promotor) and Prof. Dr. Thomas Bäck (promotor). I am currently working as a visiting PhD researcher in the DAML group at Technical University of Munich, supervised by Prof. Dr. Stephan Günnemann.

Research Interest

I am interested in the field of Data Mining and Machine Learning. During my PhD, I work on trustworthy anomaly detection, with a focus on but not limited to complex data such as event sequence and graph-structured data for smart manufacturing.

More concretely, I aim to improve the accuracy (namely make anomaly detection models more accurate, especially with high-dimensional, unstructured, multimodal data), explainability (namely make anomaly detection models more understandable for humans) and generalizability (namely make anomaly detection models work reliably under changing conditions such as data distribution shifts, uncharted hyperarameter configurations, and the presence of adversarial perturbations) of anomaly detection methods.

Recent News

• 🔥News : Our paper titled Graph Neural Networks based Log Anomaly Detection and Explanation is accepted by ICSE’24 poster track! (January 2024)
• 🔥News : Our paper titled A survey on explainable anomaly detection is accepted by TKDD for publication! (July 2023)
• 🔥News : Our paper titled Explainable Contextual Anomaly Detection using Quantile Regression Forests is accepted by DAMI for publication! (June 2023)
• [Open-Position]: I am looking for a Master student to do an internship&master thesis, which will be in collaboration with Canon. The thesis proposal is “Knowledge-based & Data-driven Inkjet Jet Failure Detection and Classification”. (October 2023)
• [Position-Filled]: I am looking for a Master student to do an internship&master thesis, which will be in collaboration with VDL & ASML. The thesis proposal is “Predictive Maintenance of Lithography System Using Log Anomaly Detection”. (June 2023)

Contact

Office 169, Niels Bohrweg 1, 2333CA, Leiden, The Netherlands

Email: z.li@liacs.leidenuniv.nl