Dr. Jinpeng Chen PhD

Dr. Jinpeng Chen is an expert in Data Mining, Machine Learning, and Social Network Analysis. His core research revolves around developing scalable and adaptive algorithms for complex graph-based data structures. His interests span cutting-edge areas like Graph Neural Networks (GNNs), Multimodal Large Language Models (MLLMs), and continual learning strategies. Dr. Chen is focused on applying his analytical expertise to real-world challenges in recommender systems, intrusion detection, and social computing, contributing valuable insights to the field of intelligent systems and data analytics.​

Education Details:

• PhD: Computer Science
   

Professional Background:

• Associate Professor (Current) - School Of Computer Science, Beijing University Of Posts And Telecommunications.
• Supervisor Of Doctorate And Master's Candidates (Current) - BUPT.
   

Achievements:

• Recognized for substantial contributions to Data Mining and Recommendation Systems technology.
• Active program committee member and reviewer for top-tier international conferences and journals in Computer Science and AI.
   

Current Research Projects:

• Scalable Graph Neural Networks: Research on making GNNs more adaptive and resilient to challenges like oversmoothing in deep architectures.
• Continual Learning For AI: Developing methods for deep networks to incrementally learn new information without forgetting previously acquired knowledge.

Academic Profiles of Jinpeng Chen:

Explore his academic and professional presence across trusted platforms:

• Google Scholar
• OpenReview
• DBLP
• Semantic Scholar
• ResearchGate

Dr. Jinpeng Chen has maintained a strong record of publications detailing novel algorithmic contributions to intelligent systems and data processing, frequently published in high-impact venues. His work is influential in advancing the state-of-the-art in scalable AI methods.

The list of recent publications of Dr. Jinpeng Chen is listed below:

• Sun, C., Hu, J., Gu, H., Chen, J., Liang, W., & Yang, M. (2025). Scalable and adaptive graph neural networks with self-label-enhanced training. Pattern Recognition, 160, 111210. DOI: 10.1016/j.patcog.2025.111210.
• Chen, J., Cong, R., Luo, Y., Ip, H. H. S., & Kwong, S. (2025). Replay Without Saving: Prototype Derivation and Distribution Rebalance for Class-Incremental Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence.
• Chen, J., Cong, R., Luo, Y., Ip, H. H. S., & Kwong, S. (2025). SEFE: Superficial and Essential Forgetting Eliminator for Multimodal Continual Instruction Tuning. ICML.
• Wang, H., Guan, W., Chen, J., Zhou, Z., & Zhou, D. (2024). Towards heterogeneous long-tailed learning: Benchmarking, metrics, and toolbox. Advances in Neural Information Processing Systems 37, 73098-73123.
• Cong, R., Xiong, H., Chen, J., Zhang, W., Huang, Q., & Zhao, Y. (2024). Query-guided prototype evolution network for few-shot segmentation. IEEE Transactions on Multimedia, 26, 6501-6512.

Last Updated on October 09, 2025