On July 16, 2026, the Cross Innovation Seminar on the Frontiers of Statistics and Artificial Intelligence was held at Shanghai University of Finance and Economics. This seminar is jointly organized by the School of Statistics and Data Science, AI Finance Development and Service Center, and Shanghai Financial Intelligence Engineering Technology Research Center of Shanghai University of Finance and Economics. More than ten experts and scholars from well-known universities at home and abroad, such as Peking University, Fudan University, University of Science and Technology of China, Xiamen University, East China Normal University, Southern University of Science and Technology, and Nanyang Technological University, were invited to conduct in-depth discussions on cutting-edge directions such as big language models, robust statistics, causal inference, and generative AI.
The meeting was chaired by Professor Zhang Liwen, Director of the AI Finance Development and Service Center, Director of the Shanghai Financial Intelligence Engineering Technology Research Center, and Professor of the School of Statistics and Data Science at Shanghai University of Finance and Economics.
The morning session was themed "Empowering Teaching, Research and Innovation with Artificial Intelligence", and was presided over by Professor Feng Xingdong, Dean of the School of Statistics and Data Science at Shanghai University of Finance and Economics, Professor Yao Fang, Director of the Statistical Science Center at Peking University, and Professor Zhu Zhongyi from Fudan University. Professor Liu Jingyuan from Xiamen University gave a keynote speech on "LLM Powered Deep Panel Modeling", focusing on big language models empowering panel data modeling and providing a new paradigm for the study of complex economic and financial time-series data; Professor Wang Xueqin, Chair Professor at the University of Science and Technology of China, systematically reviewed the cutting-edge developments in high-dimensional robust statistical theory with the title "Outlier Detection with Oracle Refits Hard Trimming via Splicing"; Professor Zhang Liwen from Shanghai University of Finance and Economics gave an in-depth explanation of the modeling methods, implementation pain points, and solutions of large language models in financial scenarios under the title of "Large Language Model in Finance Domain". In the subsequent discussion session, teachers, students, and experts engaged in lively interactions on topics such as big model statistical theory, robust modeling, and financial implementation practices.
After the meeting, the attendees collectively visited the AI Learning Center of Shanghai University of Finance and Economics, and observed the school's distinctive achievements in the application of large models and the construction of intelligent teaching and research platforms. The visiting scholars highly praised Shanghai University of Finance and Economics' exploration in the integration of AI and financial education.
The afternoon session was themed "Interdisciplinary Innovation in Statistics and Artificial Intelligence Frontiers" and was hosted by Professor Li Deyuan from Fudan University, Professor Bo Yang from Shanghai University of Finance and Economics, Professor Zhang Liwen, Associate Professor He Shen, and Associate Professor Li Ting. Professor Yu Zhou from East China Normal University gave a systematic lecture on the statistical theoretical basis and multi scenario applications of diffusion generation models, titled "Several Theories and Applications of Diffusion Generation"; Professor Tang Yanlin, Director of the Department of Statistics at the School of Statistics, East China Normal University, gave a report on "Distribution Free Prediction Sets for Regression under Target Shift", introducing the construction method of non distributed conformal prediction sets in target offset scenarios, providing new ideas for heterogeneous data prediction; Associate Professor Li Ting from Southern University of Science and Technology shared a deep sufficient modality learning framework for multimodal complex data mining under the title "DeepSuM: A Deep Sufficiently and Efficient Modal Learning Framework"; Associate Professor Li Ting from Shanghai University of Finance and Economics explained the application of functional linear structural equation models in causal inference of biomedical imaging with the title "Causal Inference in Biomedical Imaging via Functional Linear Structural Equation Models"; Dr. Gan Guangyan from Nanyang Technological University introduced the Joint Transfer Learning framework and its practical achievements in the field of asset pricing under the title of "Joint Transfer Learning with an Application to Asset Pricing". During the exchange and discussion session, attending teachers, students, and experts had in-depth discussions on the current challenges and future directions in the intersection of statistics and artificial intelligence.
This seminar showcased the cutting-edge layout and construction achievements of Shanghai University of Finance and Economics in the intersection of statistics and AI, further deepening its academic cooperation network with top universities at home and abroad, and laying a solid foundation for subsequent joint research and talent cultivation.
Contribution: Yuan Xiao
Image provided and edited by Shen Linsong
Reviewed by: Zhang Liwen
