》 Research Fields
Dawei Cheng (程大伟) is currently an associate professor appointed at the Department of Computer Science and Technology of Tongji University, Shanghai, China. I serve as dean assistant at Collaborative Innovation Center of Internet Finance Safety and guest Ph.D. supervisor at the University of Technology Sydney, Australia. I specialize in data mining, machine learning, deep learning and reinforcement learning. Now I mainly focus on deep learning in complex financial networks and various 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.
My research interest includes:
Neural Network and Deep Learning
Algorithm Analysis and Design
Financial Service Computing
1. National Science Foundation of China entitled Risk assessment by deep graph learning (Youth Foundation).
2. Shanghai Scientific and Technological Innovation Action Plan
3. National Science Foundation of China (General Foundation)
4. the National Key R&D Program of China
5. China Postdoctoral Science Foundation
1.D. Cheng, Z. Niu, J. Li, C. Jiang. Regulating systemic crises: Stemming the contagion risk in networked-loans through deep graph learning. IEEE Transactions on Knowledge and Data Engineering. 2022
2.S. Xiang, D Cheng*, et al., “Efficient Learning-based Community-Preserving Graph Generation”. ICDE. 2022
3.D. Cheng, X. Wang, Y. Zhang, S. Xiang, “Efficient Top-k Vulnerable Nodes Detection in Uncertain Graphs” IEEE Transactions on Knowledge and Data Engineering. 2021.
4.S. Xiang, D. Wen, D Cheng*, et al., “General Graph Generators: Experiments, Analyses, and Improvement” The VLDB Journal. 2021
5.D. Cheng, F. Yang, S. Xiang, J. Liu, “Financial Time Series Forecasting with Multi-Modality Graph Neural Network” Pattern Recognition. 2021.
6.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. 2020.
7.D. Cheng, Z. Niu, L. Zhang, “Delinquent Events Prediction in Temporal Networked-Guarantee Loans” IEEE Transactions on Neural Networks and Learning Systems. 2020.
8.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.
9.D. Cheng, Z. Niu, Y. Zhang, “Contagious Chain Risk Rating for Networked-guarantee Loans” ACM SIGKDD. 2020.
10.D. Cheng, F. Yang, X. Wang, Y. Zhang, L. Zhang, “Knowledge Graph-based Event Embedding Framework for Financial Quantitative Investments” ACM SIGIR. 2020.
11.D. Cheng, X. Wang, Y. Zhang, L. Zhang, “Risk Guarantee Prediction in Networked-Loans” IJCAI. 2020.
12.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.
13.Y. Tu, L. Niu, W. Zhao, D. Cheng, L. Zhang, “Image Cropping with Composition and Saliency Aware Aesthetic Score Map” AAAI. 2020.
14.Y. Tu, L. Niu, J. Chen, D. Cheng, L. Zhang, “Learning from Web Data with Self-Organizing Memory Module” CVPR. 2020.
15.X. Liang, D. Cheng, F. Yang, et.al, “F-HMTC: Detecting Financial Events for Investment Decisions Based on Neural Hierarchical Multi-Label Text Classification” IJCAI. 2020.
16.M. Fan, D. Cheng, F. Yang, et.al, “Fusing Global Domain Information and Local Semantic Information to Classify Financial Documents” CIKM. 2020.
17.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.
18.D. Cheng, Y. Tu, Z. Ma, Z. Niu, L. Zhang, “Risk Assessment for Networked-guarantee Loans Using High-order Graph Attention Representation” IJCAI. 2019.
19.D. Cheng, Y. Zhang, F. Yang, Y. Tu, Z. Niu, L. Zhang, “A dynamic default prediction framework for networked-guarantee loans” CIKM. 2019.