Biography
Dawei Cheng (程大伟) is currently an associate professor appointed at Department of Computer Science and Technology of Tongji University (同济大学), Shanghai, China. I serve as dean assistant of 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 big data in finance, deep learning in complex financial networks and big data analysis.
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.
Interests
- Big Data in Finance
- Graph Learning, Data Mining
- Deep Learning
- Reinforcement Learning
Academic Services
- (Senior) Programe Commitee of SIGKDD, ICDE, AAAI, IJCAI, CIKM, VLDB, ECML, etc.
- Reviewer of TKDE, TNNLS, INS, PR, 计算机学报, etc.
- Area Chair of AAAI, PRAI
- Distinguished Member of CCF Shanghai
News
One Paper (Corresponding) was accepted by SIGKDD 2023.
Two Papers (Corresponding) were accepted by IJCAI 2023.
We released an open source fraud detection benchmark (Antifraud).
One Paper (first author) on Group-aware GNN was accepted by TKDE 2023.
I was invited to serve as Area Chair for AAAI 2023.
I'm serving as Senior PC (Meta Reviewer) for IJCAI 2023.
Two Papers (Corresponding) were accepted by AAAI 2023.
I'm serving as TPC member (Reviewer) for AAAI and SIGKDD 2023.
One Paper (first author) was accepted by TKDE 2022.
One Paper (Corresponding) was accepted by ICDE 2022.
I'm serving as Area Chair for PRAI 2022
One Paper (Corresponding) was accepted by TASLP 2022.
I'm serving as TPC member (Reviewer) for SIGKDD and CIKM 2022.
Lanuch a Joint Research Project with China Financial Futures Exchange
Focus on event-based risk regulation via deep graph learning techniques.
担任网络金融安全国家级（省部共建）协同创新中心主任助理。
I'm serving as TPC member (Reviewer) for AAAI 2022.
Two Papers (Corresponding) were accepted by The VLDB Journal 2021.
My responsible National Science Foundation of China was approved.
Risk assessment by deep graph learning (Youth Foundation).
One Paper (first author) was accepted by TKDE 2021.
Launch Joint Reseach with China UnionPay
Fraud and money laundering detection by deep graph learning and reinforcement learning techniques.
One Paper (first & corresponding) was accepted by Pattern Recognition.
I'm attending IJCAI-PRICAI 2020.
Give a talk about financial knowledge graph for CMBC
I will deliver a training about "financial knowledge graph and its applications in banking industry" for CMBC(China Merchants Bank)
I'm serving as TPC member (Reviewer) for CIKM and AAAI 2021.
One Paper (first author) on graph learning was accepted by TKDE.
One Paper (first author) was accepted by SIGKDD 2020.
One Paper (first author) was accepted by TNNLS 2020.
I'm serving as TPC member (Reviewer) for IJCAI 2020.
Three Paper were accepted by AAAI 2020.
Lanuch a Joint Research Project with Morgan-Stanley
Focus on fraud transaction detection, specialized in suspicious transactions detection in the futures market.
One Paper on Risk Assessment was accepted by 计算机学报 2019.
One Paper on Graph Learning was accepted by IJCAI 2019.
My responsible China Postdoctoral Science Foundation was approved.
Selected Publications
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.
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S. Xiang, D. Cheng*, J. Zhang, Z. Ma, X. Wang, Y. Zhang, “Efficient Learning-based Community-Preserving Graph Generation” ICDE. 2022.
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S. Xiang, D. Wen, D. Cheng*, Y. Zhang, et.al, “General graph generators: experiments, analyses, and improvements” The VLDB Journal. 2021.
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P. Zhu, D. Cheng*, F. Yang, et. al, “Improving Chinese Named Entity Recognition by Large-Scale Syntactic Dependency Graph” IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2022.
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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.
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D Wen, B Yang, Y Zhang, L Qin, D Cheng*, W Zhang, “Span-reachability querying in large temporal graphs” The VLDB Journal. 2021.
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D. Cheng, Z. Niu, Y. Zhang, “Contagious Chain Risk Rating for Networked-guarantee Loans” ACM SIGKDD. 2020.
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D. Cheng, F. Yang, S. Xiang, J. Liu, “Financial Time Series Forecasting with Multi-Modality Graph Neural Network” Pattern Recognition. 2021.
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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.
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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.
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D. Cheng, Z. Niu, L. Zhang, “Delinquent Events Prediction in Temporal Networked-Guarantee Loans” IEEE Transactions on Neural Networks and Learning Systems. 2020.
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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.
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D. Cheng, F. Yang, X. Wang, Y. Zhang, L. Zhang, “Knowledge Graph-based Event Embedding Framework for Financial Quantitative Investments” ACM SIGIR. 2020.
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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.
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D. Cheng, X. Wang, Y. Zhang, L. Zhang, “Risk Guarantee Prediction in Networked-Loans” IJCAI. 2020.
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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.
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Y. Tu, L. Niu, W. Zhao, D. Cheng, L. Zhang, “Image Cropping with Composition and Saliency Aware Aesthetic Score Map” AAAI. 2020.
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Y. Tu, L. Niu, J. Chen, D. Cheng, L. Zhang, “Learning from Web Data with Self-Organizing Memory Module” CVPR. 2020.
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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.
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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.
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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.
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D. Cheng, Y. Tu, Z. Ma, Z. Niu, L. Zhang, “Risk Assessment for Networked-guarantee Loans Using High-order Graph Attention Representation” IJCAI. 2019.
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D. Cheng, Y. Zhang, F. Yang, Y. Tu, Z. Niu, L. Zhang, “A dynamic default prediction framework for networked-guarantee loans” CIKM. 2019.
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D. Cheng, Y. Tu, Z. Niu, L. Zhang, “Learning Temporal Relationships Between Financial Signals” ICASSP. 2018.
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Full List of Publications (Google Scholar)Full List of Publications (DBLP)
Projects
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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 Foundation.
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 Foundation 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.
Courses
Contact
- dcheng AT tongji.edu.cn
- No 4800, Cao'an Highway, Shanghai, China
- Department of Computer Science and Technology
- Monday 10:00 to 13:00
Wednesday 09:00 to 10:00