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Research interests
My current research interests include the following topics.
- Trustworthy graph machine learning (robustness, fairness, explainability, ...)
- Data-efficient graph machine learning (semi-supervised learning, self-supervised learning, active learning, ...)
- Graph Foundation Models, LLMs for graphs (in-context learning, agentic AI, alignment, RAG, ....)
- Interdisciplinary graph machine learning applications in molecular design, molecular dynamics, drug discovery and other AI4Science applications.
Projects and supervisions
Research projects
Overcoming Data Scarcity in 3D Scaffold Hopping with Diffusion Models
Principal Investigator
Description
Funded by EPSRCManaging organisational unit
School of Engineering Mathematics and TechnologyDates
01/01/2026
Publications
Recent publications
01/01/2026Graph-based Label-Efficient Learning: When Graph-Structured Data Meets Limited Labels
40th Annual AAAI Conference on Artificial Intelligence
MMPG: MoE-based Adaptive Multi-Perspective Graph Fusion for Protein Representation Learning
40th Annual AAAI Conference on Artificial Intelligence
Semi-supervised Instruction Tuning for Large Language Models on Text-Attributed Graphs.
Proceedings of the ACM Web Conference 2026
Context-aware inductive knowledge graph completion with latent type constraints and subgraph reasoning
AAAI'25/IAAI'25/EAAI'25: Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence and Thirty-Seventh Conference on Innovative Applications of Artificial Intelligence and Fifteenth Symposium on Educational Advances in Artificial Intelligence
Domain-Adapted Diffusion Model for PROTAC Linker Design Through the Lens of Density Ratio in Chemical Space
The 42nd International Conference on Machine Learning