应我院管理科学与物流系邀请，2025年7月17日下午，美国威斯康星大学张春明教授来到主楼409会议室进行了题为“Directed Acyclic Graph (DAG)-Informed Structure Learning from Multivariate Temporal Point Process Data”的学术报告，报告会由我院管理科学与物流系王建才老师主持。来自青岛大学、北京邮电大学、北京理工大学数学学院和机械车辆学院、我院管理科学与物流系的教师，以及管理科学与物流方向的博士生和硕士生10余人参加了此次报告会。

针对脉冲序列数据神经元间因果关系的推断问题，报告人提出了一种用于在大规模事件网络中学习有向无环图（DAG）结构的新方法，可应用于多维时间点过程数据。核心是条件强度函数，基于 DAG 的图参数构建了一个生成模型，并开发了一种带等式约束的估计方法，从而摆脱了依赖穷举搜索的传统方法。提出了一种新颖且灵活的增强拉格朗日优化方案，能够在保证全局收敛性的同时显著提升计算效率。此外，还通过结合无环性约束和稀疏正则化探讨了因果结构学习问题。报告会上，张教授与参会师生们积极互动，就报告中的问题进行了深入的讨论，并引导进行思考，老师和同学们踊跃提问，张教授都悉心地一一作答。

张春明教授简介：

Chunming Zhang is a Professor in the Department of Statistics at the University of Wisconsin–Madison. She earned a BS in Mathematical Statistics from Nankai University, an MS in Computational Mathematics from the Chinese Academy of Sciences, and a PhD in Statistics from the University of North Carolina at Chapel Hill. Her research focuses on statistical learning theory and methods applied to computational neuroscience, bioinformatics, and financial econometrics, alongside the analysis of imaging, spatial, and temporal data. Her work also explores dimension reduction and high-dimensional inference, multiple hypothesis testing and large-scale simultaneous inference, nonparametric and semiparametric modeling and inference, functional and longitudinal data analysis, and change-point detection. Dr. Zhang is an elected Fellow of the Institute of Mathematical Statistics (IMS) and the American Statistical Association (ASA), an elected Member of the International Statistical Institute (ISI), and a recipient of the IMS Medallion Award and Lecture (2024).  She has also served on the editorial boards of the Annals of Statistics and the Journal of the American Statistical Association.