为更好地推动统计学、数据科学及相关学科的发展，促进国内青年统计学者之间的学术交流与合作，全国工业统计学教学研究会青年统计学家协会2026年年会将于2026年4月11日在西南财经大学柳林校区（成都）举办。此次会议由全国工业统计学教学研究会青年统计学家协会主办，西南财经大学统计与数据科学学院、西南财经大学统计交叉创新研究院、西南财经大学数据科学与商业智能联合实验室承办，《统计理论及其应用（英文）》编辑部、狗熊会协办。论坛邀请国内外知名统计学家和杰出青年统计学者做大会报告，并邀请国内优秀青年统计学者到会开展深入探讨，也为有志于进入高校发展的统计学人才以及有意求贤的高校，提供互相展示、沟通和了解的交流平台（点击阅读原文下载会议通知）。
大会报告
报告人简介
寇纲 教授
寇纲现任全国政协委员、湘江实验室副主任，西南财经大学大数据研究院院长、中国系统工程学会副理事长、长江学者特聘教授、国家杰出青年科学基金获得者、国务院享受政府特殊津贴专家。主持社科重大等多项科研课题；在Science，Nature子刊，UTD24期刊（ISR, JOC）和ICML、AAAI、KDD等顶会发表200余篇论文，H指数77，论文被他人引用2万余次。以第一完成人身份获教育部高等学校科学研究优秀成果奖自然科学一等奖、人文社会科学一等奖等多项省部级科研奖励，所撰写的10余份政策建议曾获得习近平总书记等中央领导人批示。
报告题目
Recommendation Systems Leveraging Multi-view Graph Contrastive Learning and Online Distillation
报告摘要
Recommendation systems have become deeply embedded in people's daily lives, and users' heavy reliance on them poses non-negligible potential risks to mental health. Furthermore, in healthcare applications, recommendation technology has been successfully deployed across multiple domains including disease prediction, prevention, and medical diagnosis, demonstrating significant value. Typically, recommendation systems leverage rich historical interaction data between users and items to achieve accurate recommendations. However, in practical applications, new users or items often face the cold-start problem due to lack of interaction data. Simultaneously, insufficient interaction information leads to data sparsity issues. These two challenges severely constrain the performance of recommendation systems. To address these limitations, this study first proposes a multi-view graph contrastive learning approach that jointly models attributes and structure. Through an adaptive contrastive learning module, our method dynamically regulates the mutual information levels between the final contrastive views, thereby fully exploiting the rich information contained in both attribute and structural views. For the data sparsity problem, we propose a multi-view fusion recommendation framework. This framework utilizes multi-view graph contrastive learning to integrate user social relationships and item semantic associations, effectively mitigating the negative impact of data scarcity. For cold-start scenarios, we design a bidirectional online distillation mechanism. This enables two-way knowledge transfer between the content-enhanced collaborative embedding network and the content-based embedding network, achieving adaptive fusion of content information and collaborative signals. This approach effectively resolves the cold-start problem while enhancing recommendation performance.
报告人简介
常晋源 教授
常晋源，西南财经大学光华首席教授、中国科学院数学与系统科学研究院研究员，主要从事大规模复杂数据分析相关的研究，先后担任统计学、计量经济学和运筹管理国际顶级学术期刊Journal of the Royal Statistical Society Series B、Journal of Business & Economic Statistics、Journal of the American Statistical Association和Operations Research的副主编，获得过国务院政府特殊津贴、霍英东教育基金会高等院校青年教师奖一等奖和青年科学奖一等奖、教育部高等学校科学研究优秀成果奖、四川省青年科技奖等多项奖励。
报告题目
CP-factorization for high dimensional tensor time series and double projection iterations
报告摘要
We adopt the canonical polyadic (CP) decomposition to model high-dimensional tensor time series. Our primary goal is to identify and estimate the factor loadings in the CP decomposition. We propose a one-pass estimation procedure through standard eigen-analysis for a matrix constructed based on the serial dependence structure of the data. The asymptotic properties of the proposed estimator are established under a general setting as long as the factor loading vectors are algebraically linear independent, allowing the factors to be correlated and the factor loading vectors to be not nearly orthogonal. The procedure adapts to the sparsity of the factor loading vectors, accommodates weak factors, and demonstrates strong performance across a wide range of scenarios. A tractable limiting representation of the estimator is derived, which plays a key role in the related inference problems. To further reduce estimation errors, we also introduce an iterative algorithm based on a novel double projection approach. We theoretically justify the improved convergence rate of the iterative estimator, and also provide the associated limiting distribution. All results are validated through extensive simulations and a real data application.
报告人简介
周帆 副教授
周帆，上海财经大学统计与数据科学学院副教授，教育部青年长江学者，博士毕业于美国北卡罗来纳大学教堂山分校，现担任统计学顶刊JASA的编委。研究兴趣包括深度学习，强化学习的算法与理论，大模型，因果推断，在包括JASA, JMLR, NeurIPS, ICML, ICLR等统计学，机器学习顶刊和顶会上发表一作通讯文章数十篇，曾获泛华统计协会国际会议新研究者奖,UNC James E. Grizzle Distinguished Alumnus Award和Barry H. Margolin Award.
报告题目
AI for Statistics的一些最新进展
报告摘要
本报告将介绍我们近期在AI for Statistics方面的一系列探索与进展。围绕“大模型的统计推理能力”这一核心问题，我们从底层数据、知识建模与辅助研究三个层面展开研究。首先，我们构建了StatEval——首个面向统计学的综合性问答与推理的综合性数据集和评测基准，系统覆盖从本科与研究生基础知识到前沿科研级证明问题，填补了现有大模型基础数据与评测中统计学维度长期缺失的空白。其次，基于StatEval，我们进一步构建了一个由 基础知识、理论定理与研究论文三个层次组成的统计知识图谱与知识库，可与 RAG 框架结合，显著提升基线模型在统计推理与定理理解方面的能力。在此基础上，我们还开发了一个面向统计研究的AI助手，能够辅助研究者查找相关文献与定理、核查证明思路，并在一定范围内参与简单定理的构造与证明。
报告人简介
兰伟 教授
兰伟，博士毕业于北京大学光华管理学院，现为西南财经大学教授，博士生导师，统计与数据科学学院副院长，财经数智科学创新实验室主任。主要研究方向为大型网络数据分析、实证资产定价和投资组合优化。主持自科青年科学基金项目（B类）、面上项目和多个重点项目子课题。在Journal of the American Statistical Association, Annals of Statistics, Journal of Econometrics, Journal of Business & Economic Statistics，经济学季刊等国内外知名期刊发表论文60余篇。
报告题目
Quantile Social Autoregressive Model
报告摘要
Research on peer effects typically adopts linear-in-means (LIM) models. These models average responses across peers and therefore cannot capture scenarios where influence comes from better or worse peers. To address this limitation, this paper introduces a quantile social norm defined on the empirical distribution of peers’ responses and develops a novel quantile social autoregressive model. In our setting, the social norm is a peer-group quantile, which allows the data to determine which segment of peers drives individual behavior. To estimate the model, we introduce a new set of moment conditions and instruments constructed from pseudo responses. A kernel-smoothed method is further adopted to obtain the generalized method of moments (GMM)-type estimator. We systematically discuss the existence and uniqueness of equilibrium and the identification conditions. We further establish the consistency and asymptotic normality of the estimator. Monte Carlo experiments and an empirical application show gains in fit and clearer interpretation relative to linear-in-means models.
邀请报告
本次会议设置了20余个平行分会场，邀请国内外优秀青年学者近100余人.邀请报告的主题包括深度学习进展、高维统计推断、生物统计进展、复杂时间序列分析、生成模型理论与应用、数据驱动决策、网络结构数据分析、教学获奖分享、学科建设经验、AI在教学中的应用等。
博士生论坛
此次会议设有超过10场博士生论坛，欢迎在读博士生投稿（投稿要求会在稍后专门发布推文）。一旦入选将有机会在博士生论坛进行宣讲，并且得到travel award。成功宣讲的博士生会得到协会颁发的宣讲证书。
高校招聘专场
为了更好地促进高校与博士生之间的交流，协会特设高校招聘专场，费用6000元/个。2024年和2025年的年会成功吸引了超过30家高校和企业到现场进行招募。有意向的高校请将基本情况发送邮件到 feng.li@gsm.pku.edu.cn，与李老师联络。请在邮件中说明学校或学院的基本情况，联系人方式等。
会议举办地点
西南财经大学柳林校区 四川成都温江柳台大道555号
联系人：潘蕊 panrui_cufe@126.com
扫描下方二维码即可报名参会（本次会议不收取会务费，食宿等费用自理）
