students

PhD and Masters students

Current PhD Students

Hanwen Ge

Starting June 2026

Topic: Explainability for Decision Support Supported by PriXAI

Yuan Xue

Topic: Accelerating Deep RL

Yuan studies more efficient deep reinforcement learning, including graph-based abstractions for improving sample efficiency.

Graduated PhD Students

Tianqi Zhao

Graduated May 2026

Topic: Characterizing Learning Difficulty in Graph-Structured Data

Tianqi’s PhD examined how data and task complexity shape learning on graph-structured data, with a focus on robustness and reliability.

Emmanuel Iyiola Olatunji

Graduated July 2024

Topic: Privacy-Preserving Graph Machine Learning

Emmanuel studied privacy risks in graph learning and developed methods to quantify and mitigate them under privacy guarantees.

Ngan Thi Dong

Graduated July 2023

Topic: Joint Learning from Multiple Information Sources for Biological Problems

Ngan developed methods for learning jointly from omics, clinical, and biological network data to improve generalization in biomedical settings.

Master's Students (at TU Delft)

Yang Li LiGNN-LLM Hybrids for Multi-Label Node Classification
Kanta TanahashiSelf-Supervised Learning for Privacy-Preserving GNNs
Alex LalovGenerative AI for Biomedical Relational Data Generation and Imputation
Jorden van SchijndelProcess-Aware Graphs as Safety Mechanisms for AI
Nicolas Perez ZambranoExplainable Graph Learning in Biomedicine
Shuang LiuExplainability in Knowledge Graph-Based Recommender Systems
Yuchuan FuExplaining Link Prediction in GNNs
Ellemijn VernhoutLLM Support for Advance Care Planning