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.
Fair Graph Neural Network (WSDM-21) Paper Code
Privacy Preserving FairGNN (TKDE) Paper
Sensitive Attribute Protection (TKDE) Paper
Membership Privacy Protection (KDD-23) Paper
Label Noise-Resistant GNN (KDD-21) Paper Code
Defend Structural Noise (WSDM-22) Paper Code
Unnoticeable Graph Backdoor (WWW-23) Paper Code
Graph-Augmented AI for Social Good
Fake Health News Repository with Social Network Context (ICWMS-20) Paper Dataset
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
- [Video] Introduction about Unnoticeable Backdoor Attacks on Graph Neural Networks (WWW-2023)
- [Video] Introduction about robust structural noise-resistant GNN (WWW-2022)
- A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
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
News
- 09/2023:One paper entitiled: “Certifiably Robust Graph Contrastive Learning” accepted by NeurIPS-2023
- 09/2023:One paper entitled:”Learning Fair Models without Sensitive Attributes: A Generative Approach” accepted by Neurocomputing
- 05/2023: One paper entitled:”A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy” accepted by KDD-2023
- 04/2023: Serve as reviewer of KDD-2023 and ICML-2023.
- 03/2023: Very glad to receive the IST Ph.D. Student Award for Research Excellence
- 01/2023: One paper entitled: “Unnoticeable Backdoor Attacks on Graph Neural Networks” has been accepted by WWW-2023
- 11/2022: One paper entitled: “Label-Wise Graph Convolutional Network for Heterophilic Graphs” has been accepted by LOG-2022
- 08/2022: One paper has been accepted by ICDM-2022
- 07/2022: One paper entitled: “Learning Fair Graph Neural Networks with Limited andPrivate Sensitive Attribute Information” has been accepted by TKDE
- 07/2022: Update a package of Robust GNN for Label Noises [code]
- 06/2022: Serve as a reviewer for NeurIPS-2022
- 06/2022: Serve as a PC memeber for ASONAM-2022
- 06/2022: Invited as a guest for Trustworthy Graph Learning Tutorial in DataFun
- 04/2022: Release a survey entitled: “A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability”
- 03/2022: Serve as a PC memeber for KDD-2022
- 01/2022: One paper entitled “Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series” is accepted as Spotlight in ICLR-2022
- 10/2021: Two papers are accepted by WSDM-2022
- 08/2021: One paper is accepted by CIKM-2021
- 06/2021: Serve as a PC memeber for ASONAM-2021
- 05/2021: Two papers are accepted by KDD-2021
- 10/2020: One paper is accepted by WSDM-2021