About Me
Introduction
Zhong Li is a guest researcher in Computer Science at Leiden University, affiliated with the EDA Lab. I completed my PhD under the supervision of Dr. Matthijs van Leeuwen (daily supervisor and promotor) and Prof. Dr. Thomas Bäck (promotor). In 2024, I was a visiting PhD researcher at the DAML group, Technical University of Munich, supervised by Prof. Dr. Stephan Günnemann.
📘 PhD Thesis: Trustworthy Anomaly Detection for Smart Manufacturing
Research Interests
My research lies broadly in Data Mining and Machine Learning, with strong interest in AI for Math.
Postdoctoral Research
During my postdoc, I am working on generative AI for Math, with a particular focus on:
- LLM for Optimization Modeling
- Flow Matching and Diffusion Models
PhD Research
During my PhD, I focused on trustworthy anomaly detection, particularly for complex data like event sequences and graph-structured data in smart manufacturing contexts. My goals were to enhance:
- Accuracy: Improve performance with high-dimensional, unstructured, multimodal data
- Explainability: Make models more interpretable and human-friendly
- Generalizability: Ensure robustness under data shifts, unknown hyperparameter settings, and adversarial noise
Recent News
- 2025.11: 🔥🔥 Our research paper Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection is selected by AAAI journal track for presentation.
- 2025.11: 🔥🔥 One research paper is accepted by AAAI for oral presentation: Learning Subgroups with Maximum Treatment Effects without Causal Heuristics. Congratulations to Lincen.
- 2025.09: 🔥🔥 One research paper is accepted by NeurIPS: Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching
- 2025.06: 🔥🔥 One research paper is accepted by DMKD: Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection
- 2025.05: 🎓 Successfully defended my PhD thesis on Trustworthy Anomaly Detection for Smart Manufacturing at Leiden University!
📄 Read the full thesis - 2024.11: Presenting our paper Explainable Graph Neural Networks Under Fire at the Amsterdam meetup for the global LoG Conference
- 2024.09: 🔥🔥 One research paper is accepted by TKDE: Cross-domain Graph Level Anomaly Detection
- 2024.01: 🔥🔥 ICSE’24 Poster: Graph Neural Networks based Log Anomaly Detection and Explanation
- 2023.07: 🔥🔥 One survey paper is accepted by TKDD: A survey on explainable anomaly detection — 🏆 Highly cited paper based on ESI.
- 2023.07: 🔥🔥 One research paper is accepted by DMKD: Explainable Contextual Anomaly Detection using Quantile Regression Forests
- 2022.12: 🔥🔥 One research paper is accepted by SIGKDD Explorations: Feature Selection for Fault Detection and Prediction based on Event Log Analysis
- 2021.10: 🔥🔥 One research paper is accepted by Clinical Trials: Choosing and changing the analysis scale in non-inferiority trials with a binary outcome
- [
Position Cancelled] Internship & thesis opportunity with Canon on Inkjet Jet Failure Detection (Oct 2023) - [
Position Filled] Internship & thesis with VDL & ASML on Predictive Maintenance via Log Anomaly Detection (June 2023)
Professional Service
- Reviewer for the following journals:
- IEEE Transactions on Knowledge and Data Engineering (TKDE)
- Data Mining and Knowledge Discovery (DMKD / DAMI)
- International Journal of Computer Vision (IJCV)
- IEEE Transactions on Dependable and Secure Computing (TDSC)
- IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)
- IEEE Transactions on Cloud Computing (TCC)
- IEEE Internet of Things Journal (IoT)
- IEEE Intelligent Systems
- Pattern Recognition
- Measurement
- AI Communications
- PC Member or Reviewer for the following conferences:
- ECMLPKDD
- KDD
- ICLR
- AAAI
Contact
WeChat: DigitalTwinNL Email: z.li(at)liacs.leidenuniv.nl