About me
I am a tenure-track assistant professor in the AI Trust at the Hong Kong University of Science and Technology (Guangzhou). I recevied my Ph.D. degree from the Pennsylvania State University under the supervision of 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.
[Ph.D. student and Research Assistant Positions available]
I am seeking highly self-motivated Ph.D. students and Research Assistants to join my team starting in Fall 2025. Candidates with solid backgrounds in data mining, machine learning, mathematics and other related fields are encouraged to apply. If interested, please email me (enyandai@hkust-gz.edu.cn) your CV and transcript, kindly using the subject line "[Ph.D./RA Application - your name]."
Research Directions at 
(Trust & Application AI Lab)
The overview of the research directions are listed following. More details can be referred in my research statement.
Summary of Prior Works
Fair Graph Neural Network (WSDM-21) Paper Code
Privacy Preserving FairGNN (TKDE) Paper
Sensitive Attribute Protection (TKDE) Paper
Membership Privacy Protection (KDD-23) Paper
Deep IP Protection (Preprint) 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
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
- 07/2024：One paper entitled: Towards Prototype-Based Self-Explainable Graph Neural Network accepted by TKDD
- 07/2024: Two papers accepted by CIKM-2024 in full paper track and short paper track respectively.
- 07/2024：I joined the AI Thrust at Hong Kong University of Science and Technology (Guangzhou).
- 05/2024: One paper entitled:Improving Issue-PR Link Prediction via Knowledge-aware Heterogeneous Graph Learning accepted by IEEE Transactions on Software Engineering
- 05/2024: One paper entitled: Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective accepted by KDD-2024
- 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