From August 3 to 5, 2026, the Forum on Frontiers of Bayesian Statistics and Artificial Intelligence was held at the School of Management, Fudan University, in Shanghai. Organized by the Bayesian Statistics Branch of the Chinese Association for Applied Statistics and hosted by the School of Management, Fudan University, the forum brought together experts, scholars, researchers, and graduate students in Bayesian statistics, artificial intelligence, and related fields. It provided a high-level platform for academic exchange and facilitated discussions on the latest advances in Bayesian statistical theories and methods, as well as their interdisciplinary integration with artificial intelligence technologies.
Registration at the Forum
The forum featured an opening ceremony, keynote presentations, and academic presentations in parallel sessions. Professor Zhang Fuqiang, Dean of the School of Management at Fudan University; Professor Ai Mingyao, Vice President of the Chinese Association for Applied Statistics; and Professor Tang Niansheng, Vice President of Yunnan University, delivered remarks at the opening ceremony. Professor Sun Jianguo of the Southern University of Science and Technology and Professor Sun Jian of Xi’an Jiaotong University delivered keynote presentations. Participating experts engaged in in-depth discussions on Bayesian modeling and inference, statistical learning, and frontier issues in artificial intelligence, with particular emphasis on Bayesian optimization, active learning, experimental design, probabilistic inference in scientific discovery, and the analysis of hallucinations and trustworthiness in large models.
Opening Ceremony and Keynote Presentations
Academic Presentations in the Parallel Sessions
Students Kang Haowen, Huang Zhenxiang, Du Chenxin, Xu Zhimiao, Gong Haoran, and Wang Kaiqi from the team of the Anhui Provincial Engineering Research Center for Advanced Functional Polymer Films attended the forum together. During the forum, the students attentively listened to the keynote presentations and parallel-session presentations, studied the potential applications of Bayesian and artificial intelligence methods in high-throughput experimentation, materials research and development, and intelligent scientific research, and exchanged relevant research ideas with participating faculty members and students.
Students from the Engineering Research Center Attentively Listen to the Presentations and Take Notes
Methods such as Bayesian optimization, active learning, and experimental design can provide valuable methodological guidance for screening candidate formulations, optimizing experimental parameters, and supporting information-gain-driven iterative decision-making in the Engineering Research Center’s high-throughput experiments. Probabilistic inference and trustworthiness analysis also offer new research perspectives on uncertainty assessment, hallucination mitigation, and trustworthy scientific decision-making for large models in materials science. The topics covered by the forum were highly interdisciplinary and closely related to the Engineering Research Center’s ongoing work in high-throughput experimentation, intelligent materials research and development, and artificial-intelligence-enabled scientific research, providing considerable inspiration for these research activities.
Through their participation in the forum, the students gained a deeper understanding of the latest developments at the intersection of Bayesian statistics and artificial intelligence, broadened their research horizons, and enhanced their awareness of how statistical methods can support intelligent research in materials science. The knowledge and insights gained from the forum will provide useful ideas and references for the subsequent optimization of high-throughput experiments, the development of intelligent scientific research platforms, and research on large models for materials science.
Group Photo of Forum Participants
