Nelson Bighetti

Dawei Cheng

Assistant Professor

Tongji University


Dawei Cheng is currently an assistant professor appointed at Department of Computer Science and Technology in Tongji University, Shanghai, China. I specialize in data mining, machine learning, deep learning and reinforcement learning. Now I mainly focus on deep learning in compext financial networks and other big data applications.

Prior to now, I was a postdoctoral associate at Center for Brain-like Computing and Machine Intelligence (BCMI), Shanghai Jiao Tong University (SJTU) , China. Before that, I obtained my Ph.D degree in computer science from Shanghai Jiao Tong University, supervised by Prof. Liqing Zhang and bachelor degree from Nanjing University of Aeronautics and Astronautics in China.


  • Graph Learning, Data Mining
  • Big Data in Finance
  • Deep Learning
  • Reinforcement Learning

Academic Services

  • Program Commitee Member of AAAI, IJCAI, CIKM, VLDB, ECML in 2020, IJCAI, CIKM, PKDD in 2019
  • Reviewer of TKDE, TNNLS, IJPRAI, JETAI, IJNS, 计算机学报, 计算机科学
  • Distinguished Member of CCF Shanghai


I'm attending IJCAI-PRICAI 2020.

Give a talk about complex financial networks for CMBC

I will deliver a training about "complex financial networks and its applications in banking industry" for CMBC(China Merchants Bank)

I will serve as TPC member for AAAI 2021.

One Paper (first author) on graph learning was accept by TKDE.

One Paper (first author) was accept by KDD 2020.

Lanuch a joint research project with EMoney

Focus on improving the performance of quantiative investment by applying deep learning and reinforcement learning techiques on financial big data.

One Paper (first author) was accept by TNNLS 2020.

I will serve as TPC member for IJCAI 2020.

Three Paper were accept by AAAI 2020.

One Paper on Risk Assessment was accept by 计算机学报 2019.

One Paper on Graph Learning was accept by IJCAI 2019.

My responsible China Postdoctoral Science Fundation was approved.

Selected Publications

D. Cheng, X. Wang, Y. Zhang, L. Zhang, “Graph Neural Network for Fraud Detection via Spatial-temporal Attention” IEEE Transactions on Knowledge and Data Engineering. 2021. PDF
D. Cheng, Z. Niu, L. Zhang, “Delinquent Events Prediction in Temporal Networked-Guarantee Loans” IEEE Transactions on Neural Networks and Learning Systems. 2021. PDF
程大伟, 牛志彬, 刘新海, 张丽清, “复杂担保网络中传染路径的风险评估” 中国科学: 信息科学. 2021. PDF
D. Cheng, S. Xiang, C. Shang, Y. Zhang, F. Yang, L. Zhang, “Spatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection” AAAI. 2020. PDF
D. Cheng, Z. Niu, Y. Zhang, “Contagious Chain Risk Rating for Networked-guarantee Loans” ACM SIGKDD. 2020. PDF
D. Cheng, F. Yang, X. Wang, Y. Zhang, L. Zhang, “Knowledge Graph-based Event Embedding Framework for Financial Quantitative Investments” ACM SIGIR. 2020. PDF
程大伟, 牛志彬, 张丽清, “大规模不均衡担保网络贷款的风险研究” 计算机学报. Vol 43, No. 4, 2020. PDF
D. Cheng, X. Wang, Y. Zhang, L. Zhang, “Risk Guarantee Prediction in Networked-Loans” IJCAI. 2020. PDF
Z. Niu, R. Li, J. Wu, D. Cheng, J. Zhang, “iConVis: Interactive Visual Exploration of the Default Contagion Risk for Networked-guarantee Loans” IEEE VAST. 2020. PDF
Y. Tu, L. Niu, W. Zhao, D. Cheng, L. Zhang, “Image Cropping with Composition and Saliency Aware Aesthetic Score Map” AAAI. 2020. PDF
Y. Tu, L. Niu, J. Chen, D. Cheng, L. Zhang, “Learning from Web Data with Self-Organizing Memory Module” CVPR. 2020. PDF
X. Liang, D. Cheng, F. Yang, Y. Luo, W. Qian, A. Zhou, “F-HMTC: Detecting Financial Events for Investment Decisions Based on Neural Hierarchical Multi-Label Text Classification” IJCAI. 2020. PDF
M. Fan, D. Cheng, F. Yang, S. Luo, Y. Luo, W. Qian, A. Zhou, “Fusing Global Domain Information and Local Semantic Information to Classify Financial Documents” CIKM. 2020. PDF
X. Liu, Z. Niu, Y. Yang, J. Wu, D. Cheng, X. Wang, “VAP: A Visual Analysis Tool for Energy Consumption Spatio-temporal Pattern” 23rd International Conference on Extending Database Technology. 2020. PDF
Y. Zhang, L. Niu, Z. Pan, M. Luo, J. Zhang, D. Cheng, L. Zhang, “Exploiting Motion Information from Unlabeled Videos for Static Image Action Recognition.” AAAI. 2020. PDF
D. Cheng, Y. Tu, Z. Ma, Z. Niu, L. Zhang, “Risk Assessment for Networked-guarantee Loans Using High-order Graph Attention Representation” IJCAI. 2019. PDF
D. Cheng, Y. Zhang, F. Yang, Y. Tu, Z. Niu, L. Zhang, “A dynamic default prediction framework for networked-guarantee loans” CIKM. 2019. PDF
Y. Chen, J. Kuang, D. Cheng, J. Zheng, M. Gao, A. Zhou, “AgriKG: an agricultural knowledge graph and its applications” DASFAA . 2019. PDF
D. Cheng, Y. Tu, Z. Ma, Z. Niu, L. Zhang, “BHONEM: Binary high-order network embedding methods for networked-guarantee loans” Journal of Computer Science and Technology. 2019. PDF
Z. Niu, D. Cheng, L. Zhang, J. Zhang, “Visual analytics for networked-guarantee loans risk management” IEEE PacificVis. 2018. PDF
D. Cheng, Y. Liu, L. Zhang, “Exploring motor imagery EEG patterns for stroke patients with deep neural networks” ICASSP. 2018. PDF
D. Cheng, Z. Niu, Y. Tu, L. Zhang, “Prediction defaults for networked-guarantee loans” ICPR. 2018. PDF
D. Cheng, Y. Liu, Z. Niu, L. Zhang, “Modeling similarities among multi-dimensional financial time series” IEEE Access. 2018. PDF
D. Cheng, Y. Tu, Z. Niu, L. Zhang, “Learning Temporal Relationships Between Financial Signals” ICASSP. 2018. PDF
See Full List of Publications



Graph Learning on Networked-Loans

Risk assessment of SMEs, contagion chains, guarantees and networks, pattern mining of risk contagion, frequent motif in networked-loans
Sponsered by: China Postdoctoral Science Fundation.

Behavior-based Fraud Detection

Fraud detection on transaction behavior with deep graph neural network via spatial-temporal attention mechanism.
Sponsered by: Joint Reseach Program with Morgan-Stanley.

Representative Learning by Tensor Networks

Hierarchical feature representative learning by cortex tensor neural network.
Sponsered by: National Science Fundation of China.

Quantitative Investments by Knowledge Graph

Transactional behavior fraud detection with deep graph neural network via spatial-temporal attention mechanism.
Sponsered by: Joint Reseach Program with EMoney.

Knowledge Representation and Inference

The key technology of representation and common-sence inference in knowledge graphs.
Sponsered by: Shanghai Science and Technology Innovation Plan.


Financial Service Computing

A degree course for first- and second-year of postgraduate student.
Financial Service Computing


  • dcheng AT
  • No 4800, Cao'an Highway, Shanghai, China
  • College of Electronic and Information Engineering
  • Monday 10:00 to 13:00
    Wednesday 09:00 to 10:00