Name: Zhang Liwen Title: Professor
Research Interests: Statistical theory and methods, large model theory and its applications, artificial intelligence theory and methods, quantitative investment, etc.
Courses Taught: Probability Theory, Mathematical Statistics, Big Data Mining (Graduate Course), Big Data Business Practice (MBA Course), Artificial Intelligence and Quantitative Investment (MBA Course), etc.
E-mail: zhang.liwen@mail.shufe.edu.cn; Phone: 021-65901549
Research Project
Serial Number | Project Name | Project Number | Project Origin | Start and End Time |
1 | Research on High-Dimensional Quantile Regression Structural Change Point Model and Its Application in the Context of the COVID-19 Pandemic | 22BTJ031 | General Project of National Social Science (General Category) | 2022-2025 |
2 | Research on Various Types of Quantile Regression Change Points | 11601313 | National Natural Science Foundation Youth Project | 2017-2020 |
3 | New business model SUFE-GPT empowers the integration of science, education, and technology. | 2024111018 | Shanghai Municipal Education Commission Project (Provincial and Ministerial Level) | 2024-2025 |
4 | Research on the Development of Public Opinion on the COVID-19 Pandemic and Related Suggestions under the Background of Big Data | Vertical Project | Special Research Project on COVID-19 Prevention and Control at Shanghai University of Finance and Economics | 2020-2020 |
5 | Research on Change Points and Applications in Quantile Regression under High-Dimensional Data | 2017LY32 | National Statistical Science Research Project (Provincial and Ministerial Level) | 2018-2020 |
6 | Structural Change Issues in Quantile Regression Models and Their Application in Stock Modeling | ZZSD15107 | Shanghai Excellent Young Teacher Training and Funding Program | 2015-2017 |
7 | Financial Multimodal Recognition Model | Horizontal Projects | Tencent Technology (Shanghai) Co., Ltd. | 2024-2025 |
8 | Research on the Engineering System of Financial Corpus | Horizontal Project | Shanghai Kupars Technology Co., Ltd. | 2024-2024 |
9 | Financial Industry Large Model | Horizontal projects | Tencent Technology (Shanghai) Co., Ltd. | 2023-2024 |
10 | Robustness Evaluation Techniques for Decoupling Knowledge and Abilities in Financial Large Models | Horizontal Project | Ant Financial (Shanghai) Financial Information Service Co., Ltd. | 2023-2024 |
11 | The Application of Deep Learning Algorithms in Market Warning and Forecasting for Automobiles | Horizontal Project | SAIC Group Education Foundation Project | 2018-2019 |
12 | Application of Big Data in Automotive Marketing | Horizontal Project | SAIC Motor Corporation Education Foundation Project | 2017-2018 |
Research Field
Shanghai University of Finance and Economics, College of Statistics and Data Science and the Advanced Financial Institute at Dishui Lake Jointly Appointed Professor, Doctoral Supervisor, MBA/EMBA Mentor, Director of AI Financial Development and Service Center, Director of Shanghai Financial Intelligent Engineering Technology Research Center, Deputy Director of Key Laboratory of Mathematical Economics of the Ministry of Education, Vice Dean of Institute of Data Science and Statistics, Director of Center for Artificial Intelligence Financial Security and Evaluation, Chief Expert of Financial Technology at Shanghai International Financial Center Research Institute of Shanghai University of Finance and Economics, Responsible Person of Kuangshi Financial Education Large Model, Responsible Person of Financial Large Model Fin-R1, Responsible Person of Financial Large Model Evaluation FinEval. Jointly Cultivated Doctorate in Statistics with Fudan University School of Management and North Carolina State University, Postdoctoral Fellow in Statistics at The Chinese University of Hong Kong. Main Research Areas: Statistical Theory and Methods, Large Model Theory and Its Applications, Theories and Applications of Artificial Intelligence, AI Quantitative Investment, etc. Concurrently serves as a Judge for Shanghai Economic and Information Technology Big Data Project Special Funds, Council Member of China Field Big Data Statistics Branch, Council Member of Shanghai Society of Quantitative Economics, Discipline Leader of Statistics at Zhejiang College of Shanghai University of Finance and Economics, Leader Talent (Flexible) of "Kunlun Talents High-end Innovation and Entrepreneurship Talents" in Qinghai Province, etc. Around the "Corpus Construction - Technology Research and Development - Scenario Implementation - Standard Setting" four-in-one innovation model, has launched the most comprehensive financial education and financial industry dataset FinCorpus, the first open-source financial field R1-type reasoning large model Fin-R1, the first domestic financial large model evaluation system FinEval, and the breakthrough financial intelligent agent FinAgent from 0 to 1, building a closed-loop ecology covering research and development, verification, and application. At the same time, launched the first large model in domestic financial colleges that understands financial knowledge best — Kuangshi Financial Education Large Model, enhancing the model's understanding and generation capabilities of financial knowledge, and improving the level of intelligentization in financial education. In recent years, has served numerous big data, artificial intelligence, and financial investment companies, undertaken national, Shanghai municipal, and National Bureau of Statistics commissioned projects, and collaborated with Tencent Group, Ant Group, Leap Star, SAIC Motor, Guotai Junan Securities, Ping An Bank, Shanghai Kupa Technology Co., Ltd., China Academy of Information and Communications Technology, and other government departments and enterprises, covering big data analysis and decision support in areas such as government governance, finance, and marketing. Also serves as an anonymous reviewer for multiple journals.
Admission Target
In 2025, the main recruitment directions of this group are large model pre-training, financial DeepSeek R1 model, multimodal fusion, large model evaluation, quantitative finance investment, etc. Interested students are welcome to contact us. We hope to find students with the following characteristics: proactive, passionate about scientific research or practical development, diligent and hardworking. Based on performance, this group will preferentially recommend students for internships at cooperative units such as Tencent, Ant Group, LeapStar, and securities and banking financial companies. (Past students have a great opportunity to receive full-time offers.)
Shanghai University of Finance and Economics Artificial Intelligence Financial Large Model Laboratory (SUFE-AIFLM-Lab) is advocated and initiated by Professor Zhang Liwen from the School of Statistics and Data Science at Shanghai University of Finance and Economics, in collaboration with the School of Statistics and Data Science, the School of Finance, the Business School, the Urban and Rural Development Research Institute, and the Experimental Center. The laboratory has established subgroups for Data Foundation, Intelligent Engine, Quantitative Workshop, System Operation and Maintenance, and Research and Innovation Collaboration, gathering talents from multidisciplinary backgrounds including finance, statistics, artificial intelligence, and computer science. The current research team comprises more than 10 PhD holders, over 20 Master's students, as well as several front-end and back-end developers and large model engineers, possessing a solid theoretical foundation and innovative application capabilities across disciplines. For more detailed information and research achievements related to the laboratory, please refer to the laboratory's website and GitHub.
Education Background
2014.09-2015.08 The Chinese University of Hong Kong, Department of Statistics, Postdoctoral Fellow in Statistics
2012.03-2013.08 North Carolina State University, Department of Statistics, Joint PhD Program
2010.09-2014.07 Fudan University School of Management, PhD in Statistics
2007.09-2010.06 Nanjing Normal University School of Mathematics and Computer Science Master's Degree
2003.09-2007.06 Anhui Normal University, School of Mathematics and Computer Science, Bachelor's Degree
Work Experience
2024.07-Present Shanghai University of Finance and Economics, School of Statistics and Management, Department of Statistics, Professor, Doctoral Supervisor and MBA/EMBA Mentor.
2018.03-2024.06, Shanghai University of Finance and Economics, School of Statistics and Management, Department of Statistics, Associate Professor, Doctoral Supervisor and MBA/EMBA Mentor.
2015.08-2018.02, Shanghai University, School of Economics, Department of Finance, Lecturer, Master's Supervisor.
2016.07-2016.08, 2017.07-2017.08, 2018.5 University of Hong Kong, Department of Statistics and Actuarial Science, Visiting Scholar.
（*：通讯作者）
1.Liu, Z., Guo, X., Lou, F., Zeng, L., Niu, J., Wang, Z., ... & Zhang, L.* (2025). "Fin-R1": A large language model for financial reasoning through reinforcement learning. arXiv preprint arXiv:2507.17186.(https://github.com/sufe-aiflm-lab/fin-r1)
2.Guo, X., Xia, H., Liu, Z., Cao, H., Yang, Z., Liu, Z., ... & Zhang, L.* (2025). FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 6258-6292).(https://github.com/SUFE-AIFLM-Lab/FinEval)
3.Zeng, L., Lou, F., Wang, Z., Xu, J., Niu, J., Li, M., ... & Zhang, L.* (2025). FinGAIA: An End-to-End Benchmark for Evaluating AI Agents in Finance. arXiv preprint arXiv:2507.17186. (https://github.com/SUFE-AIFLM-Lab/FinEval)
4.Liu, Z., Guo, X., Xia, H., Zeng, L., Lou, F., Niu, J., ... & Zhang, L.* (2025). VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding. EMNLP，Accepted. .(https://github.com/SUFE-AIFLM-Lab/VisFinEval)
5.Huang, Q., Li, T., You, J., & Zhang, L.* (2025). Unified inference for longitudinal/functional data quantile dynamic additive models. Canadian Journal of Statistics, e70006.Accepted.
6.Yang, Z（学）, Zhang, L., Sun, S., & Liu, B. (2025). Robust change point detection for high‐dimensional linear models with tolerance for outliers and heavy tails. Canadian Journal of Statistics, 53(1), e11826.Accepted.
7.Kuang, Q., Zhu, Z., Zhang, L., & Zhou, F. (2023). Variance control for distributional reinforcement learning. The 40th International Conference on Machine Learning.(ICML’23), Honolulu, Hawaii, USA, Article No. 736, p.17874 -17895.
8.Zhou, F., Zhu, Z., Kuang, Q., & Zhang, L*.(2022). Non-decreasing quantile function network with efficient exploration for distributional reinforcement learning. International Joint Conference on Artificial Intelligence.（IJCAI’21）Montreal, Canada，Morgan Kaufmann，San Francisco, USA , p.3455-3461.
9.Zhang, L., Zhu, Z., Feng, X., & He, Y. (2022). Shrinkage quantile regression for panel data with multiple structural breaks. Canadian Journal of Statistics, 50(3), 820-851.
10.Huang, Q., You, J., & Zhang, L*. (2022). Efficient inference of longitudinal/functional data models with time‐varying additive structure. Scandinavian Journal of Statistics, 49(2), 744-771.
11.Ren, T., Shen, W., Zhang, L*., & Zhao, H. (2021). Bayesian phase II clinical trial design with noncompliance. Statistics in Medicine, 40(20), 4457-4472.
12.Gao, X., Shen, W., Zhang, L., Hu, J., Fortin, N. J., Frostig, R. D., & Ombao, H. (2021). Regularized matrix data clustering and its application to image analysis. Biometrics, 77(3), 890-902.
13.Caglayan, M. O., Xue, W., & Zhang, L.* (2020). Global investigation on the country-level idiosyncratic volatility and its determinants. Journal of Empirical Finance, 55, 143-160.
14.Zhang, L., & Zhu, Z. (2019). Estimating restricted common structural changes for panel data. Acta Mathematicae Applicatae Sinica, English Series, 35(4), 893-908.
15.Xue, W., & Zhang, L.* (2019). Revisiting the asymmetric effects of bank credit on the business cycle: A panel quantile regression approach. The Journal of Economic Asymmetries, 20, e00122.
16.He, Y., Zhang, X., & Zhang, L*. (2018). Variable selection for high dimensional gaussian copula regression model: an adaptive hypothesis testing procedure. Computational Statistics and Data Analysis, 124, 132-150.
17.Xue, W., & Zhang, L.*. (2017). Stock return autocorrelations and predictability in the Chinese stock market—Evidence from threshold quantile autoregressive models. Economic Modelling, 60, 391-401.
18.He, Y., Zhang, X., Wang, P., & Zhang, L*. (2017). High dimensional Gaussian copula graphical model with FDR control. Computational Statistics and Data Analysis, 113, 457-474.
19.Zhang, L., Wang, H. J., & Zhu, Z. (2017). Composite change point estimation for bent line quantile regression. Annals of the Institute of Statistical Mathematics, 69(1), 145-168.
20.Hu, J*., Zhang, L. and Wang, H. (2016). Sequential Model Selection Based Segmentation in Linear Regression: An Application to Array CGH Data. Biometrics, 72(3), 815-826 .
21.Zhang, L., Wang, H. J., & Zhu, Z. (2014). Testing for change points due to a covariate threshold in quantile regression. Statistica Sinica, 1859-1877.
22.Zhang, L., and Zhu, Z*. (2015).Testing for Change Points in Partially Linear Models. Acta Mathematicae Applicatae Sinica (English Series), 31(4): 879–892 .
23.Zhang Liwen*, Cheng Dongpo, Xue Wenjun, et al. (2022). Change point estimation for threshold autoregressive models under composite quantiles [J]. Science China: Mathematics, 52(01): 63-84.
24.Zhang Liwen, Zhu Zhoufan, Hao Hong. (2020). Research on Passenger Car Market Early Warning Model Based on Deep Learning [J]. Systems Science and Mathematics, 40(11): 2136-2150.
25.Zhu Zhoufan, Hao Hong, Zhang Liwen*. (2020). Prediction of the Chinese Automobile Market Based on Machine Learning and Time Series Combined Models. Statistics and Decision, 36(08), 177-180.
26.Zhang Liwen*, Cheng Dongpo, Xu Lingli. (2019). An Analysis of the Effects of Environmental Protection Policies on Smog Prevention and Control in the Context of the New Era—An Empirical Study Based on the Changes in PM2.5 Concentration. Journal of Shanghai University of Finance and Economics, 21(02), 17-29.
27. Zhang Liwen, Ni Zhongxin, He Yong*, et al. (2018). Change Point Detection in Censored Quantile Regression Models [J]. Science in China: Mathematics, 48(09): 1159-1180.
Books
Zhang Liwen, 2017, The Change Point Problem in Quantile Regression and Its Applications, Economic Management Publishing House.
Honor,Rewards
2025-08 NLPCC 2025 Conference Outstanding Paper Award, Award-winning Paper: FinTeam: A Multi-Agent Collaborative Intelligence System for Comprehensive Financial Scenarios
2025-02 Shenwan Hongyuan Teaching Award Special Prize Shanghai Education Development Foundation
2025-03 Yanshu Award—Outstanding Contribution Award Shanghai University of Finance and Economics School of Statistics and Data Science
2023-12 2023 Artificial Intelligence Large Model Benchmark Testing Science and Technology Innovation Development Conference and Central and Western China Digital Economy Development Conference Third Prize Sichuan Province Big Data Development Alliance
Social Work
Academic Professional Services:
Served as a peer reviewer for journals such as "Biometrics," "Annals of the Institute of Statistical Mathematics," "Emerging Markets Finance and Trade," "Chinese Science," and "Systems Engineering and Mathematics."
Social Service:
Shanghai Economic and Information Technology Big Data Project Special Fund Evaluator
Director of the China On-site Big Data Statistics Association
Director of the Shanghai Society of Quantitative Economics
Head of the Statistics Discipline at Zhejiang College, Shanghai University of Finance and Economics
Qinghai Province "Kunlun Talents High-end Innovation and Entrepreneurship Talent" Leading Talents (Flexible)
Report
1. "Frontier Development and Application Research of Financial Large Models," Keynote Speech, 2nd CCF China Digital Finance Conference · Financial Large Model Themed Forum (CDFC 2025), 2025-08-16, Shanghai
2. "How the Digital Economy Drives High-Quality Development of Northwest Characteristic Industries", Conference Speech, CCF YOCSEF Western Tour Event, 2025-08-10, Lanzhou
3. "Frontier Development and Application Research of Financial Large Models", Expert Forum, "Research Plan for Large Model Applications Based on Financial Business Scenarios" (FAIS) Thematic Seminar, 2025-07-07, Shanghai
4. "Research on the Cutting-edge Development and Application of Financial Large Models", Forum Speech, CCF YOCSEF Shanghai - Technology Forum, 2025-06-22, Shanghai
5. "Progress in Research and Applications of Artificial Intelligence Large Models at the Key Laboratory of Mathematical Economics, Ministry of Education," keynote speech, Seminar on How Artificial Intelligence Large Models Promote High-Quality Economic Development and Financial Large Model Advancement Meeting, 2025-05-24, Shanghai University of Finance and Economics National University Science and Technology Park
6. "DeepSeek Underlying Logic Analysis and Business Practical Path," Public Course, Shanghai University of Finance and Economics iMBA Program iFeel Public Course, 2025-03-20, Online
7. "Trends in the Application Development of Financial Large Models", Lecture, "Entering the Scene" Weekly Meeting Series Training (Financial Sector), 2025-03-04, Shanghai
8. "Current Development of Financial Large Models and Evaluation System for Large Models in the Financial Field," Academic Report, "Smart Connectivity" No. 14 Industry-Academia-Research Exchange Event, 2025-02-28, Huawei Urban Immersive Scene Innovation Center (Shanghai)
9. "Exploration of the Evaluation System for Large Models in China's Financial Sector," Conference Speech, 2025 Global Developer Pioneer Conference "Building a New Financial Ecology Together: AI Large Model Implementation and Practice" Sub-forum, 2025-02-23, Shanghai Xuhui West Bank Art Center
10. "Latest Research Results of Evaluation System for Large Models in the Financial Field," Special Topic Sharing, Shanghai University of Finance and Economics Digital Economy Discipline Development Forum · Sub-forum Three: Digital Finance — Large Model Industry Application Technology Alliance Forum, 2024-12-20, Shanghai University of Finance and Economics
11. "The Application and Challenges of Financial Large Models in the Financial Sector", Conference Speech, The 10th International Finance Technology Conference, 2024-10-26, Shanghai
12. "Where is the future of fintech? In the wave of fintech innovation, is technology leading financial innovation or is finance guiding the development of technology?", Forum speech, CCF YOCSEF Shanghai enters Shanghai University of Finance and Economics, 2024-08-03, Shanghai University of Finance and Economics
13. "FinEval: Evaluation Benchmark for Large Language Models in the Chinese Financial Field," Report, Shanghai Finance University Dishui Lake High-end Financial Conference 2024, 2024-05-25, Shanghai Lingang New Area
14. "Introduction to the Principles and Evolution of Large Language Models and Their Applications in Finance," Academic Lecture, Academic Lecture of the Economic and Financial Research Institute, Xi'an Jiaotong University Innovation Port, 2024-03-21, Xi'an Jiaotong University
15. "Introduction to the Principles and Evolution of Large Language Models and Their Applications in Finance", Academic Lecture, Academic Lecture of the Department of Statistics, Zhejiang College of Shanghai University of Finance and Economics, 2024-03-04, Zhejiang College of Shanghai University of Finance and Economics
16. "Introduction to the Principles, Evolution, and Applications of Large Language Models in Finance", Academic Seminar, Applied Mathematical Seminar 61, 2024-02-29, Shanghai University of Science and Technology
17. "Principles, Evolution, and Applications of Financial Large Models," Online Lecture, Yuxiu Lecture Hall, 2024-01-19, Tencent Meeting
18. "Theories, Development, and Applications of Large Models", Lecture, Kuangshi Lecture Hall No. 147, 2023-12-28, Zhejiang University of Finance and Economics
19. "Big Data, Artificial Intelligence and Their Applications in Financial Technology", Young Scholars Forum, "Baimai Lecture Hall" Young Scholars Forum, 2023-11-13 14:00–16:00, Shandong University of Finance and Economics
20. "The Development History and Theoretical Applications of ChatGPT," High-End Scholar Forum, "Shu Tong Jing Wei" High-End Scholar Forum, 2023-06-20, Anhui University
21. "Application of Artificial Intelligence Algorithms in Statistical Modeling," Lecture, Shanghai University of Finance and Economics Statistical Research Society Lecture, 2021-05-17, Shanghai University of Finance and Economics
22. "Time-Series Quantile Regression Models with Long Memory, Nonlinearity and Breaks", Academic Report, Special Report of the School of Mathematics and Physics, Anqing Normal University, 2019-12-19, Anqing Normal University
23. "Big Data, Artificial Intelligence and Their Applications in Financial Technology", Academic Report, School of Economics, Hefei University of Technology Academic Report, 2019-10-28, Hefei University of Technology
24. "Shrinkage Quantile Regression for Panel Data with Multiple Structural Breaks", Academic Report, School of Mathematical Sciences, Tongji University, 2017-10-13, Tongji University
