Biography
Dawei Cheng (程大伟) is currently an associate professor appointed at the School 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, generative artificial intelligence 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.
Interests
- Big Data in Finance
- Graph Learning, Data Mining
- Deep Learning and AIGC
- Deep Reinforcement Learning
Academic Services
- (Senior) Programe Commitee of ICML, ICLR, NeurIPS, SIGKDD, ICDE, AAAI, IJCAI, etc.
- Reviewer of TKDE, TNNLS, INS, PR, 计算机学报, etc.
- Area Chair of AAAI, PRAI
- Distinguished Member of CCF Shanghai
News
Two papers (Corresponding) on Graph Generation were accepted by ICDE.
Three papers (Corresponding) were accepted by NeurlPS and AAAI 2025.
担任中国计算机学会数字金融分会 (CCF Digitial Finance) 副秘书长.
Two papers (Corresponding) on Interbank were accepted by TNNLS.
My responsible National Science Foundation of China was approved.
Simulation of Risk Contagion with Generative Graph Learning.
Invited to serve as (Meta) Reviewer for NeurIPS/AAAI/KDD/IJCAI 2025.
Three papers were accepted by CIKM 2024.
One paper (Corresponding) was accepted by TKDE 2024.
Launch Joint Reseach with Tencent WeChat Pay (微信支付).
Detecting organized fraudsters with diffusion model and graph-augmented learning.
Two papers (Co-corresponding) were accepted by ICML 2024.
We freshly updated the open-source risk rating project for (Interbank).
Three papers (Corresponding) were accepted by IJCAI 2024.
I was invited to serve as (Meta) Reviewer for IJCAI/KDD/ICML 2024.
Two papers were accepted by AAAI and ICLR 2024, respectively.
Awarded Shanghai Youth Top Talent Program (上海市青年拔尖人才计划).
One Paper (Corresponding) was accepted by SIGKDD 2023.
Two Papers (Corresponding) were accepted by IJCAI 2023.
We released an open source fraud detection project (Antifraud).
One Paper (first author) on Group-aware GNN was accepted by TKDE.
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.
Awarded the ACM Shanghai Rising Star (ACM中国上海新星奖).
One Paper (first author) was accepted by TKDE 2022.
One Paper (Corresponding) was accepted by ICDE 2022.
Won WAIC Outstanding Paper Award (世界人工智能大会青年优秀论文奖).
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.
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
程大伟, 贾仁军, 李江彤, 丁志军, 蒋昌俊*, “知识增强的中文金融大模型研究” 《大数据》. 2025.
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Junyi Liu, Dawei Cheng, Changjun Jiang, “Preferential Selective-Aware Graph Neural Network for Preventing Attacks in Interbank Credit Rating” IEEE Transactions on Neural Networks and Learning Systems. 2024.
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Y Bian, X Ju, J Li, Z Xu, D Cheng*, Q Xu, “Multi-Patch Prediction: Adapting Language Models for Time Series Representation Learning” ICML. 2024.
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Dawei Cheng, Yujia Ye, Sheng Xiang, Zhenwei Ma, Ying Zhang, Changjun Jiang, “Anti-money laundering by group-aware deep graph learning” IEEE Transactions on Knowledge and Data Engineering. 2023.
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Junyi Liu, Dawei Cheng, Changjun Jiang, “Preventing Attacks in Interbank Credit Rating with Selective-aware Graph Neural Network” IJCAI. 2023.
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Dawei Cheng, Zhibin Niu, Jie Li, Changjun 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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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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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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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