Dong Chen

Dong Chen (陈东) is a PHD student at degree College of System Engineering, National University of Defence Technology (NUDT). He is supervised by Prof. Xiang Zhao in Science and Technology on Information Systems Engineering Laboratory. His research interests include graph neural networks, anomaly detection and self-supervised learning.

Email / Github /

News

[2023.12] One paper has been accepted by ICDE 2024.
[2023.06] One paper has been accepted by IJCAI 2023.
[2023.01] One paper has been accepted by TheWebConf 2023.

Research

I'm interested in graph anomaly detection, source location and graph neural learning. Representative papers are highlighted.

Fine-grained Anomaly Detection on Dynamic Graphs via Attention Alignment
Dong Chen, X. Zhao, W. Xiao,
IEEE International Conference on Data Engineering ICDE, 2024, (CCF-A)

Locate, Refine and Restore: A Progressive Enhancement Network for Camouflaged Object Detection
X. Li, J. Yang, S. Li, J. Lei, J. Zhang, Dong Chen,
International Joint Conference on Artificial Intelligence IJCAI, 2023, (CCF-A)

Graph Self-supervised Learning with Augmentation-aware Contrastive Learning
Dong Chen, X. Zhao, W. Wei, Z. Tan, W. Xiao,
International World Wide Web Conference TheWebConf, 2023, (CCF-A)

H-VAE: A Hybrid Variational AutoEncoder with Data Augmentation in Predicting CRISPR/Cas9 Off-target
Weiming Xiang*, Dong Chen*, Y. Cui, S. Peng,
IEEE International Conference on Bioinformatics and Biomedicine BIBM, 2021, (CCF-B)
Paper

Predicting CRISPR-Cas9 Off-target with Self-supervised Neural Networks
Dong Chen, W. Shu, S. Peng,
IEEE International Conference on Bioinformatics and Biomedicine BIBM, 2020, (CCF-B)
Paper

Re-ranking Answer Selection with Similarity Aggregation
Dong Chen, S. Peng, K. Li, Y. Xu, J. Zhang, X. Xie,
International ACM SIGIR Conference on Research and Development in Information Retrieval SIGIR, 2020, (CCF-A)
Paper

Service

Program Committee Member for AAAI 2024, AAAI SRRAI 2024
Reviewer for Briefings in Bioinformatics, IEEE Transactions on Neural Networks and Learning Systems, Neurocomputing

Award

Design and source code from Jon Barron's website