About me

I am a final year Ph.D student in the College of Information Sciences and Technology at Penn State University - University Park. My advisor is Dr. Suhang Wang. I obtained my Master of AI degree from Computer Science Department at KU Leuven. I received my Bachelor Degree from the University of Science and Technology of China. I am actively looking for positions in academia/industry.

Research Interests

My research interests lie in data mining, Trustworthy Graph Learning, AI for Social Good applications, Trustworhty AI for Science, and Graph Foundation Model.

Fairness

Fair Graph Neural Network (WSDM-21) Paper Code

Privacy Preserving FairGNN (TKDE) Paper

Privacy

Sensitive Attribute Protection (TKDE) Paper

Membership Privacy Protection (KDD-23) Paper

robustness
Label Noise-Resistant GNN (KDD-21) Paper Code
Defend Structural Noise (WSDM-22) Paper Code
Unnoticeable Graph Backdoor (WWW-23) Paper Code

Trustworthy Graph Learning

A Comprehensive Survey of Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability. Paper

Fake Health News Dataset Repository

Graph-Augmented AI for Social Good

Fake Health News Dataset Repository

Fake Health News Repository with Social Network Context (ICWMS-20) Paper Dataset

Graph-Augmented Anomaly Detection on Power Grids

Graph-Augmented Anomaly Detection on Power Grids (ICLR-22) Paper Code

Future Research Directions

Unified Framework of Trustworthy Graph Model

Preliminary Works:
Fairness and Sensitive Attribute Privacy (TKDE) Paper
Robustness and Membership Privacy Paper

Domain-Specific Graph Foundation Model

Preliminary Works:
Neural Architecture on Small Heterophilic Graphs (LOG-22) Paper Code
LLM-Enhanced Explainable GNN on Molecular Classification (Preprint) Paper

Trustworthy Model for Science

Preliminary Works:
Prototype-Based Explainable Molecular Classifier to Interpret the Key Pattern of Each Class (Preprint). Paper
LLM-Enhanced Explainable GNN on Molecular Classification (Preprint) Paper

Recent Blogs

Invited Talks

  • 06/2023: “Towards Trustworthy Graph Neural Networks in Fairness, Robustness, and Privacy” at University of Science and Technology of China
  • 08/2022: “Graph Structure Learning for Robustness” at Amazon
  • 06/2022: “Fairness and Explainability in Graph Neural Networks” at DataFunSummit2022

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