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 worked as a visiting PhD researcher in the DAML group at Technical University of Munich in 2024, 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 following aspects of anomaly detection methods:

• Accuracy: make anomaly detection models more accurate, especially with high-dimensional, unstructured, multimodal data;
• Explainability: make anomaly detection models more understandable for humans;
• Generalizability: make anomaly detection models work reliably under changing conditions such as data distribution shifts, uncharted hyperarameter configurations, and the presence of adversarial perturbations.

Currently, my research focus is gradually shifting to:

• Data-Centric LLMs: how to achieve better LLMs with significantly less data?
• Understanding of Unsupervised Learning: Why Self-Supervised Learning works (or not)? How to improve it?
• Diffusion Models: How to better extend diffusion models for tabular and/or discrete data?

Recent News

• 2024.11: I will give a poster demonstration of our paper Explainable Graph Neural Networks Under Fire at the the Amsterdam meetup for the global LoG Conference.
• 2024.09: 🔥🔥 Our paper titled Cross-domain Graph Level Anomaly Detection is accepted by TKDE for publication!
• 2024.01: 🔥🔥 Our paper titled Graph Neural Networks based Log Anomaly Detection and Explanation is accepted by ICSE’24 poster track!
• 2023.07: 🔥🔥 Our paper titled A survey on explainable anomaly detection is accepted by TKDD for publication!
• 2023.07: 🔥🔥 Our paper titled Explainable Contextual Anomaly Detection using Quantile Regression Forests is accepted by DMKD for publication!
• 2022.12: 🔥🔥 Our paper titled Feature Selection for Fault Detection and Prediction based on Event Log Analysis is accepted by SIGKDD Explorations for publication!
• 2021.10: 🔥🔥 Our paper titled Choosing and changing the analysis scale in non-inferiority trials with a binary outcome is accepted by Clinical Trials for publication!
• [Position-Cancelled]: 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)

Professional Services

• Invited Reviewer for the following journals:
  • IEEE Transactions on Knowledge and Data Engineering (TKDE)
  • Data Mining and Knowledge Discovery (DMKD or DAMI)
  • IEEE Internet of Things Journal (IoT)
  • IEEE Intelligent Systems
  • AI Communications
• Reviewer for the following conferences:
  • KDD conference
  • ICLR conference

Contact

Office BE 2.23, Gorlaeus Building, Einsteinweg 55, 2333 CC Leiden, The Netherlands

Email: z.li(at)liacs.leidenuniv.nl